R Markdown

DDSAnalytics Talent Management Solution:

Introduction:

Keeping employees happy in the company and hiring best match for the specific role is one of the most important aspect of Talent management. People are the backbone of any company. All the resources, infrastructures, and technology provide no value to the company without the topmost talented and skilled employees. Hence, the biggest challenges that talent management company faces every day is not being able to have enough data on attracting and retaining the talented employees. Therefore, our goal in this project is to leverage the power of data science to conduct an analysis on existing employee data to provide some interesting trends that may exists in data set, identify top factors that contribute to turnover and build a model to classify attrition and predict monthly income.

# Question to Address in Analysis: 
# Q.1 Identify factors that lead to attrition backed up by evidence provided by analysis. 

#Q. 2. Find some specific trends that may exist in the data set (e.g., “Data Scientists have the highest job satisfaction”). 

#Q.3.  Also provide any other interesting trends and observations from your analysis.
#libraries 
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DDSAnalytics <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)

head(DDSAnalytics)
##   ID Age Attrition    BusinessTravel DailyRate             Department
## 1  1  32        No     Travel_Rarely       117                  Sales
## 2  2  40        No     Travel_Rarely      1308 Research & Development
## 3  3  35        No Travel_Frequently       200 Research & Development
## 4  4  32        No     Travel_Rarely       801                  Sales
## 5  5  24        No Travel_Frequently       567 Research & Development
## 6  6  27        No Travel_Frequently       294 Research & Development
##   DistanceFromHome Education   EducationField EmployeeCount EmployeeNumber
## 1               13         4    Life Sciences             1            859
## 2               14         3          Medical             1           1128
## 3               18         2    Life Sciences             1           1412
## 4                1         4        Marketing             1           2016
## 5                2         1 Technical Degree             1           1646
## 6               10         2    Life Sciences             1            733
##   EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1                       2   Male         73              3        2
## 2                       3   Male         44              2        5
## 3                       3   Male         60              3        3
## 4                       3 Female         48              3        3
## 5                       1 Female         32              3        1
## 6                       4   Male         32              3        3
##                  JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1        Sales Executive               4      Divorced          4403
## 2      Research Director               3        Single         19626
## 3 Manufacturing Director               4        Single          9362
## 4        Sales Executive               4       Married         10422
## 5     Research Scientist               4        Single          3760
## 6 Manufacturing Director               1      Divorced          8793
##   MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1        9250                  2      Y       No                11
## 2       17544                  1      Y       No                14
## 3       19944                  2      Y       No                11
## 4       24032                  1      Y       No                19
## 5       17218                  1      Y      Yes                13
## 6        4809                  1      Y       No                21
##   PerformanceRating RelationshipSatisfaction StandardHours
## 1                 3                        3            80
## 2                 3                        1            80
## 3                 3                        3            80
## 4                 3                        3            80
## 5                 3                        3            80
## 6                 4                        3            80
##   StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 1                1                 8                     3               2
## 2                0                21                     2               4
## 3                0                10                     2               3
## 4                2                14                     3               3
## 5                0                 6                     2               3
## 6                2                 9                     4               2
##   YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 1              5                  2                       0
## 2             20                  7                       4
## 3              2                  2                       2
## 4             14                 10                       5
## 5              6                  3                       1
## 6              9                  7                       1
##   YearsWithCurrManager
## 1                    3
## 2                    9
## 3                    2
## 4                    7
## 5                    3
## 6                    7
summary(DDSAnalytics)
##        ID             Age        Attrition           BusinessTravel
##  Min.   :  1.0   Min.   :18.00   No :730   Non-Travel       : 94   
##  1st Qu.:218.2   1st Qu.:30.00   Yes:140   Travel_Frequently:158   
##  Median :435.5   Median :35.00             Travel_Rarely    :618   
##  Mean   :435.5   Mean   :36.83                                     
##  3rd Qu.:652.8   3rd Qu.:43.00                                     
##  Max.   :870.0   Max.   :60.00                                     
##                                                                    
##    DailyRate                       Department  DistanceFromHome
##  Min.   : 103.0   Human Resources       : 35   Min.   : 1.000  
##  1st Qu.: 472.5   Research & Development:562   1st Qu.: 2.000  
##  Median : 817.5   Sales                 :273   Median : 7.000  
##  Mean   : 815.2                                Mean   : 9.339  
##  3rd Qu.:1165.8                                3rd Qu.:14.000  
##  Max.   :1499.0                                Max.   :29.000  
##                                                                
##    Education              EducationField EmployeeCount EmployeeNumber  
##  Min.   :1.000   Human Resources : 15    Min.   :1     Min.   :   1.0  
##  1st Qu.:2.000   Life Sciences   :358    1st Qu.:1     1st Qu.: 477.2  
##  Median :3.000   Marketing       :100    Median :1     Median :1039.0  
##  Mean   :2.901   Medical         :270    Mean   :1     Mean   :1029.8  
##  3rd Qu.:4.000   Other           : 52    3rd Qu.:1     3rd Qu.:1561.5  
##  Max.   :5.000   Technical Degree: 75    Max.   :1     Max.   :2064.0  
##                                                                        
##  EnvironmentSatisfaction    Gender      HourlyRate     JobInvolvement 
##  Min.   :1.000           Female:354   Min.   : 30.00   Min.   :1.000  
##  1st Qu.:2.000           Male  :516   1st Qu.: 48.00   1st Qu.:2.000  
##  Median :3.000                        Median : 66.00   Median :3.000  
##  Mean   :2.701                        Mean   : 65.61   Mean   :2.723  
##  3rd Qu.:4.000                        3rd Qu.: 83.00   3rd Qu.:3.000  
##  Max.   :4.000                        Max.   :100.00   Max.   :4.000  
##                                                                       
##     JobLevel                          JobRole    JobSatisfaction
##  Min.   :1.000   Sales Executive          :200   Min.   :1.000  
##  1st Qu.:1.000   Research Scientist       :172   1st Qu.:2.000  
##  Median :2.000   Laboratory Technician    :153   Median :3.000  
##  Mean   :2.039   Manufacturing Director   : 87   Mean   :2.709  
##  3rd Qu.:3.000   Healthcare Representative: 76   3rd Qu.:4.000  
##  Max.   :5.000   Sales Representative     : 53   Max.   :4.000  
##                  (Other)                  :129                  
##   MaritalStatus MonthlyIncome    MonthlyRate    NumCompaniesWorked Over18 
##  Divorced:191   Min.   : 1081   Min.   : 2094   Min.   :0.000      Y:870  
##  Married :410   1st Qu.: 2840   1st Qu.: 8092   1st Qu.:1.000             
##  Single  :269   Median : 4946   Median :14074   Median :2.000             
##                 Mean   : 6390   Mean   :14326   Mean   :2.728             
##                 3rd Qu.: 8182   3rd Qu.:20456   3rd Qu.:4.000             
##                 Max.   :19999   Max.   :26997   Max.   :9.000             
##                                                                           
##  OverTime  PercentSalaryHike PerformanceRating RelationshipSatisfaction
##  No :618   Min.   :11.0      Min.   :3.000     Min.   :1.000           
##  Yes:252   1st Qu.:12.0      1st Qu.:3.000     1st Qu.:2.000           
##            Median :14.0      Median :3.000     Median :3.000           
##            Mean   :15.2      Mean   :3.152     Mean   :2.707           
##            3rd Qu.:18.0      3rd Qu.:3.000     3rd Qu.:4.000           
##            Max.   :25.0      Max.   :4.000     Max.   :4.000           
##                                                                        
##  StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
##  Min.   :80    Min.   :0.0000   Min.   : 0.00     Min.   :0.000        
##  1st Qu.:80    1st Qu.:0.0000   1st Qu.: 6.00     1st Qu.:2.000        
##  Median :80    Median :1.0000   Median :10.00     Median :3.000        
##  Mean   :80    Mean   :0.7839   Mean   :11.05     Mean   :2.832        
##  3rd Qu.:80    3rd Qu.:1.0000   3rd Qu.:15.00     3rd Qu.:3.000        
##  Max.   :80    Max.   :3.0000   Max.   :40.00     Max.   :6.000        
##                                                                        
##  WorkLifeBalance YearsAtCompany   YearsInCurrentRole
##  Min.   :1.000   Min.   : 0.000   Min.   : 0.000    
##  1st Qu.:2.000   1st Qu.: 3.000   1st Qu.: 2.000    
##  Median :3.000   Median : 5.000   Median : 3.000    
##  Mean   :2.782   Mean   : 6.962   Mean   : 4.205    
##  3rd Qu.:3.000   3rd Qu.:10.000   3rd Qu.: 7.000    
##  Max.   :4.000   Max.   :40.000   Max.   :18.000    
##                                                     
##  YearsSinceLastPromotion YearsWithCurrManager
##  Min.   : 0.000          Min.   : 0.00       
##  1st Qu.: 0.000          1st Qu.: 2.00       
##  Median : 1.000          Median : 3.00       
##  Mean   : 2.169          Mean   : 4.14       
##  3rd Qu.: 3.000          3rd Qu.: 7.00       
##  Max.   :15.000          Max.   :17.00       
## 
colnames(DDSAnalytics)
##  [1] "ID"                       "Age"                     
##  [3] "Attrition"                "BusinessTravel"          
##  [5] "DailyRate"                "Department"              
##  [7] "DistanceFromHome"         "Education"               
##  [9] "EducationField"           "EmployeeCount"           
## [11] "EmployeeNumber"           "EnvironmentSatisfaction" 
## [13] "Gender"                   "HourlyRate"              
## [15] "JobInvolvement"           "JobLevel"                
## [17] "JobRole"                  "JobSatisfaction"         
## [19] "MaritalStatus"            "MonthlyIncome"           
## [21] "MonthlyRate"              "NumCompaniesWorked"      
## [23] "Over18"                   "OverTime"                
## [25] "PercentSalaryHike"        "PerformanceRating"       
## [27] "RelationshipSatisfaction" "StandardHours"           
## [29] "StockOptionLevel"         "TotalWorkingYears"       
## [31] "TrainingTimesLastYear"    "WorkLifeBalance"         
## [33] "YearsAtCompany"           "YearsInCurrentRole"      
## [35] "YearsSinceLastPromotion"  "YearsWithCurrManager"
dim(DDSAnalytics)
## [1] 870  36
str(DDSAnalytics)
## 'data.frame':    870 obs. of  36 variables:
##  $ ID                      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Age                     : int  32 40 35 32 24 27 41 37 34 34 ...
##  $ Attrition               : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ BusinessTravel          : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
##  $ DailyRate               : int  117 1308 200 801 567 294 1283 309 1333 653 ...
##  $ Department              : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
##  $ DistanceFromHome        : int  13 14 18 1 2 10 5 10 10 10 ...
##  $ Education               : int  4 3 2 4 1 2 5 4 4 4 ...
##  $ EducationField          : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
##  $ EmployeeCount           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ EmployeeNumber          : int  859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
##  $ EnvironmentSatisfaction : int  2 3 3 3 1 4 2 4 3 4 ...
##  $ Gender                  : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
##  $ HourlyRate              : int  73 44 60 48 32 32 90 88 87 92 ...
##  $ JobInvolvement          : int  3 2 3 3 3 3 4 2 3 2 ...
##  $ JobLevel                : int  2 5 3 3 1 3 1 2 1 2 ...
##  $ JobRole                 : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
##  $ JobSatisfaction         : int  4 3 4 4 4 1 3 4 3 3 ...
##  $ MaritalStatus           : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
##  $ MonthlyIncome           : int  4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
##  $ MonthlyRate             : int  9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
##  $ NumCompaniesWorked      : int  2 1 2 1 1 1 2 2 1 1 ...
##  $ Over18                  : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
##  $ OverTime                : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
##  $ PercentSalaryHike       : int  11 14 11 19 13 21 12 14 19 14 ...
##  $ PerformanceRating       : int  3 3 3 3 3 4 3 3 3 3 ...
##  $ RelationshipSatisfaction: int  3 1 3 3 3 3 1 3 4 2 ...
##  $ StandardHours           : int  80 80 80 80 80 80 80 80 80 80 ...
##  $ StockOptionLevel        : int  1 0 0 2 0 2 0 3 1 1 ...
##  $ TotalWorkingYears       : int  8 21 10 14 6 9 7 8 1 8 ...
##  $ TrainingTimesLastYear   : int  3 2 2 3 2 4 5 5 2 3 ...
##  $ WorkLifeBalance         : int  2 4 3 3 3 2 2 3 3 2 ...
##  $ YearsAtCompany          : int  5 20 2 14 6 9 4 1 1 8 ...
##  $ YearsInCurrentRole      : int  2 7 2 10 3 7 2 0 1 2 ...
##  $ YearsSinceLastPromotion : int  0 4 2 5 1 1 0 0 0 7 ...
##  $ YearsWithCurrManager    : int  3 9 2 7 3 7 3 0 0 7 ...

EDA Section

DDSAnalytics%>%ggplot(aes(x=YearsAtCompany, y=MonthlyIncome,color=JobRole))+
  geom_point()+ggtitle("Year at Company vs. Monthly Income")

# Bar plot of employees by thier Job role.
summary(DDSAnalytics$JobRole)
## Healthcare Representative           Human Resources 
##                        76                        27 
##     Laboratory Technician                   Manager 
##                       153                        51 
##    Manufacturing Director         Research Director 
##                        87                        51 
##        Research Scientist           Sales Executive 
##                       172                       200 
##      Sales Representative 
##                        53
jobRoleTable <- table(DDSAnalytics$JobRole)
jobRoleTableDf <- data.frame(jobRoleTable)
names(jobRoleTableDf) <- c("JobRole","TotalCount")
#What we would like is for R to respect the order in data.frame. For that to happen, we need to change the order of factor levels by specifying the order explicitly.
jobRoleTableDf$JobRole <- factor(jobRoleTableDf$JobRole,levels = jobRoleTableDf$JobRole[order(jobRoleTableDf$TotalCount)])

# library(dplyr)
# jobRoleTableDf%>%ggplot(aes(x=JobRole,y=TotalCount,fill=Jobsatisfaction))+geom_bar(stat = "identity")+coord_flip()+ggtitle("Number of Employe by Job Role")+
#   xlab("Job Role")+ylab("Total number of Employees")+theme(
#     plot.title = element_text(color = "black",
#                               hjust = 0.5,
#                               size = 15),
#     legend.position = "none"
#   )

DDSAnalytics%>%ggplot(aes(x=Department,fill=Department))+geom_bar()

DDSAnalytics%>%ggplot(aes(x=Education,fill=EducationField))+geom_bar()

DDSAnalytics%>%ggplot(aes(x=JobSatisfaction,fill=EducationField))+geom_bar()

# Job satisfaction is higher for Life Science field. 
# Job Satisfaction higher for employees with the job title Sales executive. 
# there are very few employee in Job satisfaction level 4 with job title Human Resource. 
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=JobSatisfaction,fill=JobRole))+
  geom_bar(stat = "identity")

#jobSatisfaction
plyr::count(DDSAnalytics,'JobSatisfaction')->jobSatisfactionFreq

data.frame(jobSatisfactionFreq)
##   JobSatisfaction freq
## 1               1  179
## 2               2  166
## 3               3  254
## 4               4  271
jobSatisfactionFreq%>%ggplot(aes(x=JobSatisfaction,y=freq))+
  geom_bar(stat = "identity")

# Over all the employee are satisfied with their job. 

hist(DDSAnalytics$MonthlyIncome)

hist(DDSAnalytics$MonthlyRate)

hist(DDSAnalytics$YearsAtCompany)

hist(DDSAnalytics$YearsInCurrentRole)

hist(DDSAnalytics$TotalWorkingYears)

hist(DDSAnalytics$DistanceFromHome)

DDSAnalytics%>%ggplot(aes(x=JobSatisfaction,y=YearsAtCompany,color=JobRole))+geom_point()

str(DDSAnalytics$Age)
##  int [1:870] 32 40 35 32 24 27 41 37 34 34 ...
DDSAnalytics$Age
##   [1] 32 40 35 32 24 27 41 37 34 34 43 28 35 30 46 31 32 46 34 44 36 48 43
##  [24] 31 33 44 38 33 53 52 32 33 46 35 32 32 35 36 56 31 58 45 49 42 23 42
##  [47] 32 38 18 27 38 58 44 45 31 41 42 29 37 25 36 36 31 18 24 32 35 23 35
##  [70] 37 41 28 35 41 52 30 34 46 45 31 37 35 38 37 36 59 47 35 27 46 38 29
##  [93] 37 35 50 42 46 37 29 34 27 31 34 23 53 26 29 55 46 32 28 52 52 35 35
## [116] 37 25 29 41 34 36 21 45 38 29 35 32 45 43 46 29 49 42 39 43 40 29 54
## [139] 39 37 56 58 46 37 40 29 33 33 24 28 31 32 32 41 34 30 40 39 43 42 30
## [162] 28 33 42 35 20 49 33 52 29 30 47 46 33 53 21 55 26 32 38 35 38 43 39
## [185] 39 28 25 47 50 28 30 31 46 48 26 51 51 49 44 24 40 32 32 28 32 44 43
## [208] 38 30 41 41 42 37 42 27 40 38 48 30 26 34 45 33 28 38 30 35 22 24 49
## [231] 45 35 47 54 43 37 42 32 25 50 42 45 29 25 29 29 31 32 41 41 33 36 45
## [254] 31 40 43 31 30 34 25 31 29 33 29 24 35 34 32 33 30 45 34 27 36 55 56
## [277] 22 43 25 55 43 41 40 31 38 33 22 34 36 35 45 36 27 47 40 35 34 34 31
## [300] 28 32 35 55 35 58 24 50 34 55 25 34 40 31 51 60 47 34 24 39 19 32 34
## [323] 30 46 39 27 44 42 38 42 49 32 44 27 59 33 35 34 50 27 36 28 33 30 30
## [346] 39 34 26 44 56 42 31 40 40 41 34 26 29 32 36 52 29 52 40 55 56 34 40
## [369] 23 32 31 30 45 37 20 50 36 42 29 30 47 26 48 23 51 38 35 29 36 21 34
## [392] 26 31 60 42 31 46 34 56 44 30 42 47 36 35 43 31 36 53 43 36 41 37 32
## [415] 34 35 30 36 37 29 34 37 31 18 28 43 30 21 32 34 27 40 49 29 40 27 28
## [438] 26 50 38 32 29 28 44 46 47 38 42 38 45 33 44 35 33 48 40 31 51 56 36
## [461] 42 36 50 31 43 38 36 36 26 53 27 29 45 45 28 46 37 26 52 37 29 31 34
## [484] 56 58 41 30 31 48 34 49 50 29 35 36 51 45 37 36 39 58 28 48 29 47 32
## [507] 33 51 37 41 38 39 32 33 21 51 25 42 30 56 34 28 45 49 59 53 45 31 35
## [530] 56 38 36 29 32 29 43 38 42 24 42 30 33 27 38 34 34 49 40 38 45 41 28
## [553] 36 48 40 37 54 34 42 26 36 58 27 31 30 44 32 30 42 50 41 21 28 34 26
## [576] 37 38 44 39 29 25 42 38 28 44 40 38 36 37 35 22 34 28 49 40 33 35 40
## [599] 27 24 24 32 30 29 29 53 59 38 26 33 35 41 33 19 30 34 33 44 39 35 20
## [622] 47 34 35 28 27 24 38 55 34 55 34 38 36 43 35 42 34 24 27 30 40 55 34
## [645] 41 40 18 26 35 40 35 60 40 48 22 26 51 54 41 36 32 34 46 35 34 29 29
## [668] 33 32 54 37 33 28 35 27 46 36 40 36 31 25 43 36 57 28 31 29 33 19 38
## [691] 38 47 42 30 37 47 48 36 18 46 29 34 35 26 59 41 54 31 38 50 30 57 48
## [714] 36 36 24 30 28 35 28 37 37 34 36 42 25 37 34 26 22 43 39 27 32 34 27
## [737] 39 59 35 35 25 31 37 23 25 24 29 34 24 33 20 31 50 46 54 44 35 49 38
## [760] 45 29 47 41 50 34 45 41 40 46 45 33 46 39 36 42 23 30 32 26 29 41 39
## [783] 33 33 47 31 51 52 55 25 49 34 38 36 49 33 45 22 19 36 33 31 39 35 44
## [806] 36 25 22 30 27 30 25 24 47 38 33 30 33 30 28 32 29 34 31 36 46 31 34
## [829] 38 50 45 19 28 19 55 37 37 34 47 48 18 29 52 34 34 28 19 39 40 26 31
## [852] 30 29 28 35 35 30 31 41 44 51 43 34 47 45 48 32 47 45 35
summary(DDSAnalytics$Age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   18.00   30.00   35.00   36.83   43.00   60.00
hist(DDSAnalytics$Age)

#employee training programs, identifying high-potential employees and reducing/preventing voluntary employee turnover (attrition). 
as.factor(DDSAnalytics$TrainingTimesLastYear)->traingTimeFactor
as.factor(DDSAnalytics$JobInvolvement)->jobInvFactor

dfTrainTimJobInv <- data.frame(JobInv=DDSAnalytics$JobInvolvement,
                               Training=traingTimeFactor,
                               EmployeeJobSatisfaction=DDSAnalytics$JobSatisfaction,
                               jobInvFac= jobInvFactor)
#dfTrainTimJobInv%>%ggplot(aes(x=JobInv))+geom_bar(aes(fill=Training))
library(GGally)
dfTrainTimJobInv%>%dplyr::select(JobInv,Training,EmployeeJobSatisfaction)%>%
  ggpairs(aes(color=Training))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

dfTrainTimJobInv%>%ggplot(aes(Training))+geom_bar(aes(fill=jobInvFac)) ->df1

#EDA, Correlation and Variable Selection

#Correlation 
DDSAnalytics%>%dplyr::select(Age, DailyRate,DistanceFromHome,
                      Education, EnvironmentSatisfaction,
                      HourlyRate,JobInvolvement,JobLevel,
                      JobSatisfaction, MonthlyIncome,MonthlyRate, 
                      NumCompaniesWorked,PerformanceRating,TotalWorkingYears,
                      RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
                      WorkLifeBalance,YearsInCurrentRole, YearsSinceLastPromotion,
                      YearsAtCompany, YearsWithCurrManager,
                      PercentSalaryHike,HourlyRate)->corVarSelection
#cor(k,method = "pearson")
#corrplot(DDSAnalytics, method = "number")
#k2 <- rcorr(as.matrix(k))
library(corrplot)
cor_selected_Var <-cor(corVarSelection)
head(round(cor_selected_Var,2))
##                           Age DailyRate DistanceFromHome Education
## Age                      1.00      0.01             0.01      0.22
## DailyRate                0.01      1.00             0.01     -0.01
## DistanceFromHome         0.01      0.01             1.00      0.05
## Education                0.22     -0.01             0.05      1.00
## EnvironmentSatisfaction -0.01     -0.01            -0.04     -0.04
## HourlyRate               0.05      0.05             0.07      0.01
##                         EnvironmentSatisfaction HourlyRate JobInvolvement
## Age                                       -0.01       0.05           0.02
## DailyRate                                 -0.01       0.05           0.06
## DistanceFromHome                          -0.04       0.07           0.00
## Education                                 -0.04       0.01           0.03
## EnvironmentSatisfaction                    1.00      -0.03           0.00
## HourlyRate                                -0.03       1.00           0.07
##                         JobLevel JobSatisfaction MonthlyIncome MonthlyRate
## Age                         0.48           -0.02          0.48        0.07
## DailyRate                   0.00            0.00          0.00       -0.03
## DistanceFromHome            0.02           -0.02         -0.01       -0.01
## Education                   0.13            0.01          0.13       -0.02
## EnvironmentSatisfaction     0.00           -0.02         -0.02        0.06
## HourlyRate                 -0.01           -0.09          0.00       -0.02
##                         NumCompaniesWorked PerformanceRating
## Age                                   0.29             -0.04
## DailyRate                             0.05             -0.02
## DistanceFromHome                     -0.05              0.03
## Education                             0.16             -0.03
## EnvironmentSatisfaction               0.01              0.00
## HourlyRate                            0.01              0.00
##                         TotalWorkingYears RelationshipSatisfaction
## Age                                  0.65                    -0.01
## DailyRate                           -0.01                     0.01
## DistanceFromHome                     0.00                     0.04
## Education                            0.15                    -0.03
## EnvironmentSatisfaction             -0.02                     0.00
## HourlyRate                           0.03                     0.02
##                         StockOptionLevel TrainingTimesLastYear
## Age                                 0.04                 -0.05
## DailyRate                           0.02                 -0.01
## DistanceFromHome                    0.07                 -0.04
## Education                           0.03                 -0.06
## EnvironmentSatisfaction             0.03                 -0.01
## HourlyRate                          0.06                  0.01
##                         WorkLifeBalance YearsInCurrentRole
## Age                               -0.01               0.21
## DailyRate                         -0.03               0.00
## DistanceFromHome                  -0.01              -0.01
## Education                          0.01               0.06
## EnvironmentSatisfaction            0.08               0.02
## HourlyRate                        -0.03               0.00
##                         YearsSinceLastPromotion YearsAtCompany
## Age                                        0.22           0.29
## DailyRate                                 -0.06          -0.04
## DistanceFromHome                          -0.02          -0.02
## Education                                  0.07           0.06
## EnvironmentSatisfaction                    0.01          -0.02
## HourlyRate                                 0.01           0.00
##                         YearsWithCurrManager PercentSalaryHike
## Age                                     0.19             -0.03
## DailyRate                              -0.02              0.03
## DistanceFromHome                       -0.02              0.05
## Education                               0.09              0.00
## EnvironmentSatisfaction                -0.02              0.00
## HourlyRate                              0.00             -0.02
corrplot(cor_selected_Var,method = "circle")

corrplot(cor_selected_Var,method = "number") # correlation check for all variables 

# Displays diagnoal histogram 
library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## 
## Attaching package: 'xts'
## The following objects are masked from 'package:data.table':
## 
##     first, last
## The following objects are masked from 'package:gdata':
## 
##     first, last
## The following objects are masked from 'package:dplyr':
## 
##     first, last
## 
## Attaching package: 'PerformanceAnalytics'
## The following objects are masked from 'package:e1071':
## 
##     kurtosis, skewness
## The following object is masked from 'package:graphics':
## 
##     legend
chart.Correlation(cor_selected_Var,histogram = TRUE, pac=19)

DDSAnalytics%>%ggplot(aes(x=Age,y=TotalWorkingYears,color=Gender))+geom_point()

DDSAnalytics%>%ggplot(aes(x=JobLevel,y=MonthlyIncome,color=Department))+geom_point()

DDSAnalytics%>%ggplot(aes(x=JobLevel,y=TotalWorkingYears))+geom_point()

DDSAnalytics%>%ggplot(aes(x=TotalWorkingYears,y=MonthlyIncome,color=Gender))+geom_point()

DDSAnalytics%>%ggplot(aes(x=YearsInCurrentRole,y=YearsWithCurrManager,color=Gender))+geom_point()

DDSAnalytics%>%ggplot(aes(x=YearsSinceLastPromotion,y=YearsAtCompany,color=Gender))+geom_point()

DDSAnalytics%>%dplyr::select(Age,MonthlyIncome,TotalWorkingYears,PercentSalaryHike,YearsWithCurrManager,
                      YearsInCurrentRole,YearsSinceLastPromotion,MonthlyRate,NumCompaniesWorked,
                      RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
                      WorkLifeBalance,YearsAtCompany)->selected_var 



selected_var_correlation <- cor(selected_var)

head(round(selected_var_correlation,2))
##                        Age MonthlyIncome TotalWorkingYears
## Age                   1.00          0.48              0.65
## MonthlyIncome         0.48          1.00              0.78
## TotalWorkingYears     0.65          0.78              1.00
## PercentSalaryHike    -0.03         -0.05             -0.06
## YearsWithCurrManager  0.19          0.33              0.46
## YearsInCurrentRole    0.21          0.36              0.49
##                      PercentSalaryHike YearsWithCurrManager
## Age                              -0.03                 0.19
## MonthlyIncome                    -0.05                 0.33
## TotalWorkingYears                -0.06                 0.46
## PercentSalaryHike                 1.00                -0.05
## YearsWithCurrManager             -0.05                 1.00
## YearsInCurrentRole               -0.02                 0.71
##                      YearsInCurrentRole YearsSinceLastPromotion
## Age                                0.21                    0.22
## MonthlyIncome                      0.36                    0.32
## TotalWorkingYears                  0.49                    0.45
## PercentSalaryHike                 -0.02                   -0.07
## YearsWithCurrManager               0.71                    0.51
## YearsInCurrentRole                 1.00                    0.55
##                      MonthlyRate NumCompaniesWorked
## Age                         0.07               0.29
## MonthlyIncome               0.06               0.16
## TotalWorkingYears           0.06               0.26
## PercentSalaryHike           0.00              -0.02
## YearsWithCurrManager       -0.02              -0.12
## YearsInCurrentRole          0.03              -0.10
##                      RelationshipSatisfaction StockOptionLevel
## Age                                     -0.01             0.04
## MonthlyIncome                            0.00             0.02
## TotalWorkingYears                       -0.02             0.04
## PercentSalaryHike                       -0.05             0.00
## YearsWithCurrManager                    -0.02             0.04
## YearsInCurrentRole                       0.00             0.08
##                      TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Age                                  -0.05           -0.01           0.29
## MonthlyIncome                        -0.04            0.02           0.49
## TotalWorkingYears                    -0.04            0.02           0.64
## PercentSalaryHike                     0.00            0.01          -0.06
## YearsWithCurrManager                  0.00            0.02           0.77
## YearsInCurrentRole                   -0.02            0.08           0.78
corrplot(selected_var_correlation,method = "circle")

corrplot(selected_var_correlation,method = "number")

# selected variables with no correlation 
DDSAnalytics%>%dplyr::select(Age,PercentSalaryHike,MonthlyRate,NumCompaniesWorked,
                      RelationshipSatisfaction,StockOptionLevel,TrainingTimesLastYear,
                      WorkLifeBalance,YearsAtCompany,DistanceFromHome,
                      Education,JobInvolvement,HourlyRate)->selected_var_correlation1

# selected_var_correlation1$MonthlyIncome_log = log(selected_var_correlation1$MonthlyIncome)
# selected_var_correlation1$MonthlyRate_log = log(selected_var_correlation1$MonthlyRate) # log made it left swequed. 


selected_var_correlation1_cor <- cor(selected_var_correlation1)
head(round(selected_var_correlation1_cor,2))
##                            Age PercentSalaryHike MonthlyRate
## Age                       1.00             -0.03        0.07
## PercentSalaryHike        -0.03              1.00        0.00
## MonthlyRate               0.07              0.00        1.00
## NumCompaniesWorked        0.29             -0.02        0.02
## RelationshipSatisfaction -0.01             -0.05       -0.02
## StockOptionLevel          0.04              0.00       -0.04
##                          NumCompaniesWorked RelationshipSatisfaction
## Age                                    0.29                    -0.01
## PercentSalaryHike                     -0.02                    -0.05
## MonthlyRate                            0.02                    -0.02
## NumCompaniesWorked                     1.00                     0.04
## RelationshipSatisfaction               0.04                     1.00
## StockOptionLevel                       0.03                    -0.03
##                          StockOptionLevel TrainingTimesLastYear
## Age                                  0.04                 -0.05
## PercentSalaryHike                    0.00                  0.00
## MonthlyRate                         -0.04                 -0.01
## NumCompaniesWorked                   0.03                 -0.07
## RelationshipSatisfaction            -0.03                  0.02
## StockOptionLevel                     1.00                  0.02
##                          WorkLifeBalance YearsAtCompany DistanceFromHome
## Age                                -0.01           0.29             0.01
## PercentSalaryHike                   0.01          -0.06             0.05
## MonthlyRate                         0.01          -0.02            -0.01
## NumCompaniesWorked                  0.02          -0.14            -0.05
## RelationshipSatisfaction            0.04           0.01             0.04
## StockOptionLevel                    0.05           0.03             0.07
##                          Education JobInvolvement HourlyRate
## Age                           0.22           0.02       0.05
## PercentSalaryHike             0.00           0.01      -0.02
## MonthlyRate                  -0.02          -0.02      -0.02
## NumCompaniesWorked            0.16          -0.01       0.01
## RelationshipSatisfaction     -0.03           0.02       0.02
## StockOptionLevel              0.03           0.07       0.06
corrplot(selected_var_correlation1_cor,method = "number")

chart.Correlation(selected_var_correlation1_cor,histogram = TRUE, pac=19)

library(ggpubr)
## Loading required package: magrittr
## 
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
## 
##     set_names
## The following object is masked from 'package:tidyr':
## 
##     extract
## 
## Attaching package: 'ggpubr'
## The following object is masked from 'package:cowplot':
## 
##     get_legend
## The following objects are masked from 'package:qwraps2':
## 
##     mean_ci, mean_sd, median_iqr

#Variable Selection and Model Fitting

library(MASS)


#DDSAnalytics <- read.csv(file.choose())
# Fit the model 
fitMod <- lm(MonthlyIncome~Age+DailyRate+PercentSalaryHike+MonthlyRate+NumCompaniesWorked+
                      RelationshipSatisfaction+StockOptionLevel+TrainingTimesLastYear+
                      WorkLifeBalance+YearsAtCompany+DistanceFromHome+
                      Education+JobInvolvement+HourlyRate,data = DDSAnalytics)
 # Stepwise variable selection 
#Variables selected are:  Age MonthlyRate NumCompaniesWorked YearsAtCompany
modStepw <- stepAIC(fitMod,direction = "both")
## Start:  AIC=14279.99
## MonthlyIncome ~ Age + DailyRate + PercentSalaryHike + MonthlyRate + 
##     NumCompaniesWorked + RelationshipSatisfaction + StockOptionLevel + 
##     TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany + 
##     DistanceFromHome + Education + JobInvolvement + HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - DailyRate                 1    1063345 1.1298e+10 14278
## - DistanceFromHome          1    1120616 1.1298e+10 14278
## - RelationshipSatisfaction  1    2121077 1.1299e+10 14278
## - Education                 1    2140131 1.1299e+10 14278
## - WorkLifeBalance           1    2206959 1.1299e+10 14278
## - StockOptionLevel          1    2264705 1.1299e+10 14278
## - JobInvolvement            1    4092665 1.1301e+10 14278
## - HourlyRate                1    4993530 1.1302e+10 14278
## - PercentSalaryHike         1    6789217 1.1304e+10 14278
## - TrainingTimesLastYear     1    8966200 1.1306e+10 14279
## <none>                                   1.1297e+10 14280
## - MonthlyRate               1   39077410 1.1336e+10 14281
## - NumCompaniesWorked        1  206505811 1.1503e+10 14294
## - Age                       1 1483997780 1.2781e+10 14385
## - YearsAtCompany            1 2679215399 1.3976e+10 14463
## 
## Step:  AIC=14278.08
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     RelationshipSatisfaction + StockOptionLevel + TrainingTimesLastYear + 
##     WorkLifeBalance + YearsAtCompany + DistanceFromHome + Education + 
##     JobInvolvement + HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - DistanceFromHome          1    1145851 1.1299e+10 14276
## - Education                 1    2064590 1.1300e+10 14276
## - RelationshipSatisfaction  1    2086685 1.1300e+10 14276
## - WorkLifeBalance           1    2117587 1.1300e+10 14276
## - StockOptionLevel          1    2214071 1.1300e+10 14276
## - JobInvolvement            1    4337161 1.1302e+10 14276
## - HourlyRate                1    4814904 1.1303e+10 14276
## - PercentSalaryHike         1    6644296 1.1305e+10 14277
## - TrainingTimesLastYear     1    9007046 1.1307e+10 14277
## <none>                                   1.1298e+10 14278
## - MonthlyRate               1   38752514 1.1337e+10 14279
## + DailyRate                 1    1063345 1.1297e+10 14280
## - NumCompaniesWorked        1  208285019 1.1506e+10 14292
## - Age                       1 1484842711 1.2783e+10 14384
## - YearsAtCompany            1 2678231646 1.3976e+10 14461
## 
## Step:  AIC=14276.16
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     RelationshipSatisfaction + StockOptionLevel + TrainingTimesLastYear + 
##     WorkLifeBalance + YearsAtCompany + Education + JobInvolvement + 
##     HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - RelationshipSatisfaction  1    1947945 1.1301e+10 14274
## - StockOptionLevel          1    1997929 1.1301e+10 14274
## - WorkLifeBalance           1    2073908 1.1301e+10 14274
## - Education                 1    2244098 1.1301e+10 14274
## - JobInvolvement            1    4246236 1.1303e+10 14274
## - HourlyRate                1    4538008 1.1304e+10 14274
## - PercentSalaryHike         1    6359489 1.1305e+10 14275
## - TrainingTimesLastYear     1    9294642 1.1308e+10 14275
## <none>                                   1.1299e+10 14276
## - MonthlyRate               1   38700725 1.1338e+10 14277
## + DistanceFromHome          1    1145851 1.1298e+10 14278
## + DailyRate                 1    1088580 1.1298e+10 14278
## - NumCompaniesWorked        1  207140743 1.1506e+10 14290
## - Age                       1 1487126021 1.2786e+10 14382
## - YearsAtCompany            1 2677897927 1.3977e+10 14459
## 
## Step:  AIC=14274.31
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     StockOptionLevel + TrainingTimesLastYear + WorkLifeBalance + 
##     YearsAtCompany + Education + JobInvolvement + HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - StockOptionLevel          1    1848939 1.1303e+10 14272
## - WorkLifeBalance           1    1928217 1.1303e+10 14272
## - Education                 1    2376371 1.1303e+10 14272
## - JobInvolvement            1    4126458 1.1305e+10 14273
## - HourlyRate                1    4636501 1.1306e+10 14273
## - PercentSalaryHike         1    6063184 1.1307e+10 14273
## - TrainingTimesLastYear     1    9437706 1.1311e+10 14273
## <none>                                   1.1301e+10 14274
## - MonthlyRate               1   39151712 1.1340e+10 14275
## + RelationshipSatisfaction  1    1947945 1.1299e+10 14276
## + DailyRate                 1    1053348 1.1300e+10 14276
## + DistanceFromHome          1    1007112 1.1300e+10 14276
## - NumCompaniesWorked        1  205607025 1.1507e+10 14288
## - Age                       1 1489380826 1.2790e+10 14380
## - YearsAtCompany            1 2676081706 1.3977e+10 14457
## 
## Step:  AIC=14272.46
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany + 
##     Education + JobInvolvement + HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - WorkLifeBalance           1    1759117 1.1305e+10 14271
## - Education                 1    2308802 1.1305e+10 14271
## - JobInvolvement            1    3777372 1.1307e+10 14271
## - HourlyRate                1    5002592 1.1308e+10 14271
## - PercentSalaryHike         1    6049458 1.1309e+10 14271
## - TrainingTimesLastYear     1    9615796 1.1313e+10 14271
## <none>                                   1.1303e+10 14272
## - MonthlyRate               1   39818844 1.1343e+10 14274
## + StockOptionLevel          1    1848939 1.1301e+10 14274
## + RelationshipSatisfaction  1    1798956 1.1301e+10 14274
## + DailyRate                 1    1006075 1.1302e+10 14274
## + DistanceFromHome          1     817380 1.1302e+10 14274
## - NumCompaniesWorked        1  204777164 1.1508e+10 14286
## - Age                       1 1487744081 1.2791e+10 14378
## - YearsAtCompany            1 2674278411 1.3977e+10 14455
## 
## Step:  AIC=14270.59
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     TrainingTimesLastYear + YearsAtCompany + Education + JobInvolvement + 
##     HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - Education                 1    2349298 1.1307e+10 14269
## - JobInvolvement            1    3842262 1.1309e+10 14269
## - HourlyRate                1    5153532 1.1310e+10 14269
## - PercentSalaryHike         1    5990888 1.1311e+10 14269
## - TrainingTimesLastYear     1    9432510 1.1314e+10 14269
## <none>                                   1.1305e+10 14271
## - MonthlyRate               1   39984889 1.1345e+10 14272
## + WorkLifeBalance           1    1759117 1.1303e+10 14272
## + StockOptionLevel          1    1679840 1.1303e+10 14272
## + RelationshipSatisfaction  1    1671684 1.1303e+10 14272
## + DailyRate                 1     929403 1.1304e+10 14272
## + DistanceFromHome          1     795656 1.1304e+10 14272
## - NumCompaniesWorked        1  206424162 1.1511e+10 14284
## - Age                       1 1485987397 1.2791e+10 14376
## - YearsAtCompany            1 2684987116 1.3990e+10 14454
## 
## Step:  AIC=14268.77
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     TrainingTimesLastYear + YearsAtCompany + JobInvolvement + 
##     HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - JobInvolvement            1    4032699 1.1311e+10 14267
## - HourlyRate                1    5198903 1.1312e+10 14267
## - PercentSalaryHike         1    5939200 1.1313e+10 14267
## - TrainingTimesLastYear     1    9923213 1.1317e+10 14268
## <none>                                   1.1307e+10 14269
## - MonthlyRate               1   39351114 1.1346e+10 14270
## + Education                 1    2349298 1.1305e+10 14271
## + WorkLifeBalance           1    1799613 1.1305e+10 14271
## + RelationshipSatisfaction  1    1794779 1.1305e+10 14271
## + StockOptionLevel          1    1613162 1.1305e+10 14271
## + DistanceFromHome          1     949046 1.1306e+10 14271
## + DailyRate                 1     854948 1.1306e+10 14271
## - NumCompaniesWorked        1  213428005 1.1520e+10 14283
## - Age                       1 1544879419 1.2852e+10 14378
## - YearsAtCompany            1 2691269215 1.3998e+10 14452
## 
## Step:  AIC=14267.08
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     TrainingTimesLastYear + YearsAtCompany + HourlyRate
## 
##                            Df  Sum of Sq        RSS   AIC
## - HourlyRate                1    4608935 1.1316e+10 14265
## - PercentSalaryHike         1    5810127 1.1317e+10 14266
## - TrainingTimesLastYear     1   10211076 1.1321e+10 14266
## <none>                                   1.1311e+10 14267
## - MonthlyRate               1   38847658 1.1350e+10 14268
## + JobInvolvement            1    4032699 1.1307e+10 14269
## + Education                 1    2539735 1.1309e+10 14269
## + WorkLifeBalance           1    1868554 1.1309e+10 14269
## + RelationshipSatisfaction  1    1697863 1.1309e+10 14269
## + StockOptionLevel          1    1273880 1.1310e+10 14269
## + DailyRate                 1    1068020 1.1310e+10 14269
## + DistanceFromHome          1     896886 1.1310e+10 14269
## - NumCompaniesWorked        1  212128122 1.1523e+10 14281
## - Age                       1 1551816172 1.2863e+10 14377
## - YearsAtCompany            1 2687657896 1.3999e+10 14451
## 
## Step:  AIC=14265.44
## MonthlyIncome ~ Age + PercentSalaryHike + MonthlyRate + NumCompaniesWorked + 
##     TrainingTimesLastYear + YearsAtCompany
## 
##                            Df  Sum of Sq        RSS   AIC
## - PercentSalaryHike         1    5586325 1.1321e+10 14264
## - TrainingTimesLastYear     1   10326155 1.1326e+10 14264
## <none>                                   1.1316e+10 14265
## - MonthlyRate               1   39414999 1.1355e+10 14266
## + HourlyRate                1    4608935 1.1311e+10 14267
## + JobInvolvement            1    3442732 1.1312e+10 14267
## + Education                 1    2569654 1.1313e+10 14267
## + WorkLifeBalance           1    2010778 1.1314e+10 14267
## + RelationshipSatisfaction  1    1780010 1.1314e+10 14267
## + StockOptionLevel          1    1586224 1.1314e+10 14267
## + DailyRate                 1     864891 1.1315e+10 14267
## + DistanceFromHome          1     639252 1.1315e+10 14267
## - NumCompaniesWorked        1  212501684 1.1528e+10 14280
## - Age                       1 1547207287 1.2863e+10 14375
## - YearsAtCompany            1 2692826523 1.4009e+10 14449
## 
## Step:  AIC=14263.87
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + TrainingTimesLastYear + 
##     YearsAtCompany
## 
##                            Df  Sum of Sq        RSS   AIC
## - TrainingTimesLastYear     1   10333102 1.1332e+10 14263
## <none>                                   1.1321e+10 14264
## - MonthlyRate               1   39373688 1.1361e+10 14265
## + PercentSalaryHike         1    5586325 1.1316e+10 14265
## + HourlyRate                1    4385133 1.1317e+10 14266
## + JobInvolvement            1    3339411 1.1318e+10 14266
## + Education                 1    2513386 1.1319e+10 14266
## + WorkLifeBalance           1    1945145 1.1319e+10 14266
## + StockOptionLevel          1    1574609 1.1320e+10 14266
## + RelationshipSatisfaction  1    1509312 1.1320e+10 14266
## + DailyRate                 1     748081 1.1321e+10 14266
## + DistanceFromHome          1     456620 1.1321e+10 14266
## - NumCompaniesWorked        1  214556393 1.1536e+10 14278
## - Age                       1 1548022772 1.2869e+10 14373
## - YearsAtCompany            1 2715028200 1.4036e+10 14449
## 
## Step:  AIC=14262.66
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + YearsAtCompany
## 
##                            Df  Sum of Sq        RSS   AIC
## <none>                                   1.1332e+10 14263
## - MonthlyRate               1   39763836 1.1371e+10 14264
## + TrainingTimesLastYear     1   10333102 1.1321e+10 14264
## + PercentSalaryHike         1    5593272 1.1326e+10 14264
## + HourlyRate                1    4497282 1.1327e+10 14264
## + JobInvolvement            1    3595979 1.1328e+10 14264
## + Education                 1    3038550 1.1329e+10 14264
## + WorkLifeBalance           1    1751465 1.1330e+10 14264
## + StockOptionLevel          1    1738488 1.1330e+10 14264
## + RelationshipSatisfaction  1    1651462 1.1330e+10 14264
## + DailyRate                 1     785720 1.1331e+10 14265
## + DistanceFromHome          1     653090 1.1331e+10 14265
## - NumCompaniesWorked        1  220217707 1.1552e+10 14277
## - Age                       1 1557187987 1.2889e+10 14373
## - YearsAtCompany            1 2709372536 1.4041e+10 14447
modStepw$anova
## Stepwise Model Path 
## Analysis of Deviance Table
## 
## Initial Model:
## MonthlyIncome ~ Age + DailyRate + PercentSalaryHike + MonthlyRate + 
##     NumCompaniesWorked + RelationshipSatisfaction + StockOptionLevel + 
##     TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany + 
##     DistanceFromHome + Education + JobInvolvement + HourlyRate
## 
## Final Model:
## MonthlyIncome ~ Age + MonthlyRate + NumCompaniesWorked + YearsAtCompany
## 
## 
##                          Step Df Deviance Resid. Df  Resid. Dev      AIC
## 1                                               855 11296930706 14279.99
## 2                 - DailyRate  1  1063345       856 11297994051 14278.08
## 3          - DistanceFromHome  1  1145851       857 11299139902 14276.16
## 4  - RelationshipSatisfaction  1  1947945       858 11301087847 14274.31
## 5          - StockOptionLevel  1  1848939       859 11302936786 14272.46
## 6           - WorkLifeBalance  1  1759117       860 11304695904 14270.59
## 7                 - Education  1  2349298       861 11307045202 14268.77
## 8            - JobInvolvement  1  4032699       862 11311077901 14267.08
## 9                - HourlyRate  1  4608935       863 11315686836 14265.44
## 10        - PercentSalaryHike  1  5586325       864 11321273161 14263.87
## 11    - TrainingTimesLastYear  1 10333102       865 11331606263 14262.66
# histogram and qqplot of residuals 
hist(modStepw$residuals,col="orange",main = "Residual Histogram")

ggqqplot(modStepw$residuals,color = "orange",title = "Residual Normality Check") # qqplot 

plot(modStepw$residuals,col="orange",main = "Residual Plots")

#Cross Validation

# Spliting dataset into Training and Test and Cross Validation 
set.seed(5)
ddsObs = sample(seq(1,dim(DDSAnalytics)[1]),round(.75*dim(DDSAnalytics)[1]),replace = FALSE)
ddsTrain = DDSAnalytics[ddsObs,]
ddsTrain
##      ID Age Attrition    BusinessTravel DailyRate             Department
## 834 834  19        No     Travel_Rarely       645 Research & Development
## 697 697  48        No Travel_Frequently       365 Research & Development
## 207 207  43       Yes     Travel_Rarely      1372                  Sales
## 715 715  36        No        Non-Travel      1434                  Sales
## 809 809  30        No     Travel_Rarely       793 Research & Development
## 725 725  42        No     Travel_Rarely      1142 Research & Development
## 222 222  45       Yes Travel_Frequently       306                  Sales
## 71   71  41        No     Travel_Rarely       642 Research & Development
## 503 503  48       Yes Travel_Frequently       708                  Sales
## 403 403  47        No     Travel_Rarely       207 Research & Development
## 387 387  35        No Travel_Frequently       944                  Sales
## 806 806  36        No     Travel_Rarely      1383 Research & Development
## 826 826  46        No     Travel_Rarely       430 Research & Development
## 821 821  32        No     Travel_Rarely       634 Research & Development
## 844 844  34        No     Travel_Rarely       943 Research & Development
## 528 528  31        No     Travel_Rarely      1332 Research & Development
## 128 128  45        No     Travel_Rarely       556 Research & Development
## 539 539  24       Yes     Travel_Rarely      1448                  Sales
## 457 457  31        No     Travel_Rarely      1112                  Sales
## 732 732  39       Yes     Travel_Rarely      1162                  Sales
## 842 842  29        No     Travel_Rarely       332        Human Resources
## 85   85  36        No     Travel_Rarely       429 Research & Development
## 122 122  21        No     Travel_Rarely       501                  Sales
## 474 474  45        No     Travel_Rarely      1316 Research & Development
## 819 819  30        No     Travel_Rarely      1427 Research & Development
## 625 625  28       Yes Travel_Frequently       289 Research & Development
## 184 184  39        No     Travel_Rarely      1089 Research & Development
## 92   92  29       Yes     Travel_Rarely       408                  Sales
## 269 269  33        No Travel_Frequently      1303 Research & Development
## 463 463  50        No Travel_Frequently       809                  Sales
## 407 407  31        No     Travel_Rarely       196                  Sales
## 235 235  43        No Travel_Frequently       394                  Sales
## 54   54  45        No        Non-Travel       805 Research & Development
## 146 146  29        No        Non-Travel       746                  Sales
## 464 464  31        No Travel_Frequently       163 Research & Development
## 192 192  31        No Travel_Frequently      1125                  Sales
## 665 665  34        No        Non-Travel      1065                  Sales
## 394 394  60        No Travel_Frequently      1499                  Sales
## 810 810  27        No     Travel_Rarely      1157 Research & Development
## 512 512  39        No        Non-Travel      1485 Research & Development
## 628 628  38        No     Travel_Rarely       343 Research & Development
## 248 248  32        No     Travel_Rarely       234                  Sales
## 8     8  37        No     Travel_Rarely       309                  Sales
## 468 468  36        No     Travel_Rarely       132 Research & Development
## 851 851  31       Yes     Travel_Rarely       542                  Sales
## 404 404  36        No     Travel_Rarely       711 Research & Development
## 365 365  55        No     Travel_Rarely       282 Research & Development
## 491 491  49        No Travel_Frequently       636 Research & Development
## 616 616  34        No     Travel_Rarely      1157 Research & Development
## 748 748  34        No Travel_Frequently       669 Research & Development
## 123 123  45        No     Travel_Rarely       930                  Sales
## 841 841  18       Yes     Travel_Rarely       230 Research & Development
## 326 326  27        No     Travel_Rarely       618 Research & Development
## 149 149  24        No     Travel_Rarely       477 Research & Development
## 91   91  38        No Travel_Frequently      1490 Research & Development
## 72   72  28        No     Travel_Rarely       866                  Sales
## 563 563  27        No     Travel_Rarely      1134 Research & Development
## 80   80  31        No     Travel_Rarely      1276 Research & Development
## 828 828  34        No     Travel_Rarely       131                  Sales
## 481 481  29        No     Travel_Rarely       136 Research & Development
## 466 466  38        No     Travel_Rarely       371 Research & Development
## 510 510  41       Yes     Travel_Rarely      1360 Research & Development
## 381 381  47        No     Travel_Rarely      1482 Research & Development
## 90   90  46        No     Travel_Rarely       705                  Sales
## 127 127  32       Yes Travel_Frequently      1125 Research & Development
## 737 737  39        No     Travel_Rarely       722                  Sales
## 527 527  45        No     Travel_Rarely      1268                  Sales
## 100 100  34        No     Travel_Rarely       401 Research & Development
## 209 209  30        No     Travel_Rarely       201 Research & Development
## 148 148  33        No     Travel_Rarely      1242                  Sales
## 125 125  29        No Travel_Frequently       442                  Sales
## 754 754  46       Yes     Travel_Rarely      1254                  Sales
## 139 139  39       Yes     Travel_Rarely      1122 Research & Development
## 649 649  35        No     Travel_Rarely       776                  Sales
## 516 516  51        No     Travel_Rarely      1302 Research & Development
## 3     3  35        No Travel_Frequently       200 Research & Development
## 412 412  41        No     Travel_Rarely       918                  Sales
## 667 667  29        No Travel_Frequently      1413                  Sales
## 227 227  35        No     Travel_Rarely       464 Research & Development
## 4     4  32        No     Travel_Rarely       801                  Sales
## 27   27  38        No     Travel_Rarely      1245                  Sales
## 240 240  50        No     Travel_Rarely       813 Research & Development
## 169 169  52        No     Travel_Rarely       956 Research & Development
## 422 422  37        No        Non-Travel      1063 Research & Development
## 130 130  46        No     Travel_Rarely      1319                  Sales
## 784 784  33        No Travel_Frequently      1146                  Sales
## 188 188  47        No     Travel_Rarely      1225                  Sales
## 521 521  34        No Travel_Frequently       702 Research & Development
## 745 745  25        No     Travel_Rarely      1382                  Sales
## 256 256  43        No        Non-Travel       343 Research & Development
## 145 145  40        No     Travel_Rarely      1202 Research & Development
## 262 262  29       Yes     Travel_Rarely       224 Research & Development
## 369 369  23        No     Travel_Rarely       977 Research & Development
## 573 573  28        No Travel_Frequently       783                  Sales
## 135 135  43        No Travel_Frequently       559 Research & Development
## 152 152  32        No     Travel_Rarely       929                  Sales
## 374 374  37        No     Travel_Rarely      1470 Research & Development
## 822 822  29        No     Travel_Rarely      1247                  Sales
## 785 785  47        No        Non-Travel       543                  Sales
## 446 446  47        No     Travel_Rarely       465 Research & Development
## 793 793  38        No     Travel_Rarely       397 Research & Development
## 17   17  32        No     Travel_Rarely       498 Research & Development
## 718 718  28        No     Travel_Rarely       857 Research & Development
## 176 176  21        No     Travel_Rarely       984 Research & Development
## 319 319  39        No     Travel_Rarely      1498                  Sales
## 372 372  30        No        Non-Travel      1398                  Sales
## 461 461  42        No Travel_Frequently      1474 Research & Development
## 361 361  52        No     Travel_Rarely       699 Research & Development
## 141 141  56        No     Travel_Rarely      1369 Research & Development
## 28   28  33       Yes     Travel_Rarely       603                  Sales
## 423 423  31       Yes     Travel_Rarely       249                  Sales
## 363 363  52       Yes     Travel_Rarely       266                  Sales
## 796 796  33       Yes Travel_Frequently      1076 Research & Development
## 859 859  41        No     Travel_Rarely       933 Research & Development
## 295 295  40        No     Travel_Rarely       898        Human Resources
## 574 574  34        No     Travel_Rarely       810                  Sales
## 703 703  35        No        Non-Travel      1212                  Sales
## 334 334  27        No     Travel_Rarely       608 Research & Development
## 268 268  32       Yes     Travel_Rarely      1259 Research & Development
## 272 272  34        No     Travel_Rarely      1130 Research & Development
## 671 671  37        No     Travel_Rarely       397 Research & Development
## 51   51  38        No     Travel_Rarely       362 Research & Development
## 153 153  32        No     Travel_Rarely       906                  Sales
## 441 441  32        No Travel_Frequently      1316 Research & Development
## 154 154  41        No     Travel_Rarely       447 Research & Development
## 87   87  47        No     Travel_Rarely       703                  Sales
## 575 575  26        No     Travel_Rarely       703                  Sales
## 414 414  32        No Travel_Frequently      1318                  Sales
## 763 763  41        No        Non-Travel       256                  Sales
## 58   58  29        No     Travel_Rarely      1370 Research & Development
## 224 224  28        No     Travel_Rarely      1423 Research & Development
## 580 580  29        No     Travel_Rarely      1329 Research & Development
## 622 622  47        No Travel_Frequently      1379 Research & Development
## 155 155  34        No     Travel_Rarely       546 Research & Development
## 847 847  19        No     Travel_Rarely      1181 Research & Development
## 542 542  33        No     Travel_Rarely       867 Research & Development
## 395 395  42        No Travel_Frequently      1271 Research & Development
## 67   67  35        No     Travel_Rarely      1017 Research & Development
## 25   25  33        No     Travel_Rarely      1069 Research & Development
## 288 288  34        No     Travel_Rarely      1346 Research & Development
## 358 358  29       Yes     Travel_Rarely       408 Research & Development
## 827 827  31       Yes     Travel_Rarely      1079                  Sales
## 728 728  34        No Travel_Frequently       878 Research & Development
## 730 730  22        No     Travel_Rarely       594 Research & Development
## 303 303  55        No     Travel_Rarely      1441 Research & Development
## 69   69  35        No Travel_Frequently       664 Research & Development
## 264 264  29        No Travel_Frequently      1404                  Sales
## 562 562  58        No Travel_Frequently      1216 Research & Development
## 675 675  27        No     Travel_Rarely       486 Research & Development
## 427 427  30        No        Non-Travel       990 Research & Development
## 223 223  33        No        Non-Travel       530                  Sales
## 35   35  32        No     Travel_Rarely      1373 Research & Development
## 533 533  29        No     Travel_Rarely      1252 Research & Development
## 239 239  25        No     Travel_Rarely       309        Human Resources
## 243 243  29        No     Travel_Rarely      1328 Research & Development
## 707 707  54        No     Travel_Rarely       431 Research & Development
## 43   43  49        No     Travel_Rarely       464 Research & Development
## 777 777  30        No     Travel_Rarely       330        Human Resources
## 419 419  37        No     Travel_Rarely       977 Research & Development
## 333 333  44       Yes Travel_Frequently       429 Research & Development
## 640 640  27       Yes     Travel_Rarely      1420                  Sales
## 757 757  35        No     Travel_Rarely      1137 Research & Development
## 774 774  36       Yes     Travel_Rarely       318 Research & Development
## 864 864  47        No Travel_Frequently       217                  Sales
## 499 499  36        No     Travel_Rarely      1299 Research & Development
## 484 484  56        No     Travel_Rarely       713 Research & Development
## 281 281  43        No     Travel_Rarely      1179                  Sales
## 84   84  37        No     Travel_Rarely      1319 Research & Development
## 761 761  29       Yes     Travel_Rarely       906 Research & Development
## 37   37  35       Yes     Travel_Rarely       737                  Sales
## 292 292  36        No        Non-Travel      1105 Research & Development
## 537 537  38        No     Travel_Rarely       130                  Sales
## 118 118  29        No     Travel_Rarely      1378 Research & Development
## 261 261  31       Yes Travel_Frequently       703                  Sales
## 213 213  37        No     Travel_Rarely      1225 Research & Development
## 459 459  56        No     Travel_Rarely      1255 Research & Development
## 593 593  28       Yes Travel_Frequently      1496                  Sales
## 448 448  42        No     Travel_Rarely      1128 Research & Development
## 26   26  44        No     Travel_Rarely      1099                  Sales
## 599 599  27        No     Travel_Rarely       155 Research & Development
## 113 113  52        No     Travel_Rarely       994 Research & Development
## 345 345  30        No     Travel_Rarely      1329                  Sales
## 870 870  35        No Travel_Frequently       138 Research & Development
## 177 177  55       Yes     Travel_Rarely       725 Research & Development
## 156 156  30        No        Non-Travel       641                  Sales
## 568 568  30       Yes     Travel_Rarely       740                  Sales
## 673 673  28        No Travel_Frequently       467                  Sales
## 432 432  40        No        Non-Travel       458 Research & Development
## 759 759  38        No     Travel_Rarely      1495 Research & Development
## 440 440  38        No     Travel_Rarely       827 Research & Development
## 347 347  34        No Travel_Frequently      1003 Research & Development
## 833 833  28        No     Travel_Rarely      1179 Research & Development
## 274 274  36        No     Travel_Rarely       329                  Sales
## 40   40  31        No Travel_Frequently       793                  Sales
## 636 636  35        No Travel_Frequently       636 Research & Development
## 2     2  40        No     Travel_Rarely      1308 Research & Development
## 216 216  40        No     Travel_Rarely       896 Research & Development
## 312 312  40        No     Travel_Rarely       302 Research & Development
## 305 305  58       Yes     Travel_Rarely       289 Research & Development
## 838 838  34        No     Travel_Rarely       258                  Sales
## 686 686  31        No     Travel_Rarely      1274 Research & Development
## 162 162  28       Yes        Non-Travel      1366 Research & Development
## 866 866  48        No     Travel_Rarely       855 Research & Development
## 832 832  19       Yes     Travel_Rarely       528                  Sales
## 627 627  24        No     Travel_Rarely      1476                  Sales
## 329 329  38        No Travel_Frequently      1394 Research & Development
## 179 179  32        No Travel_Frequently       967                  Sales
## 545 545  34        No     Travel_Rarely       121 Research & Development
## 397 397  46        No     Travel_Rarely      1009 Research & Development
## 185 185  39        No Travel_Frequently       443 Research & Development
## 357 357  26        No     Travel_Rarely       841 Research & Development
## 170 170  29        No     Travel_Rarely      1107 Research & Development
## 251 251  33        No        Non-Travel      1313 Research & Development
## 638 638  34        No     Travel_Rarely      1239                  Sales
## 869 869  45        No     Travel_Rarely      1457 Research & Development
## 429 429  32       Yes     Travel_Rarely       515 Research & Development
## 57   57  42       Yes Travel_Frequently       933 Research & Development
## 705 705  59        No     Travel_Rarely       818        Human Resources
## 55   55  31        No     Travel_Rarely       746 Research & Development
## 853 853  29        No     Travel_Rarely       153 Research & Development
## 478 478  26        No     Travel_Rarely       775                  Sales
## 797 797  45        No     Travel_Rarely      1385 Research & Development
## 770 770  45        No        Non-Travel      1050                  Sales
## 257 257  31        No Travel_Frequently       444                  Sales
## 79   79  45        No        Non-Travel      1052                  Sales
## 66   66  32        No     Travel_Rarely       334 Research & Development
## 687 687  29       Yes Travel_Frequently       337 Research & Development
## 339 339  50       Yes Travel_Frequently       959                  Sales
## 631 631  55        No     Travel_Rarely       836 Research & Development
## 676 676  46        No     Travel_Rarely       168                  Sales
## 143 143  46        No     Travel_Rarely       566 Research & Development
## 561 561  36        No Travel_Frequently      1480 Research & Development
## 254 254  31       Yes     Travel_Rarely       359        Human Resources
## 189 189  50        No     Travel_Rarely       797 Research & Development
## 1     1  32        No     Travel_Rarely       117                  Sales
## 858 858  31        No Travel_Frequently       798 Research & Development
## 578 578  44        No        Non-Travel       981 Research & Development
## 234 234  54        No     Travel_Rarely      1147                  Sales
## 751 751  20       Yes Travel_Frequently       769                  Sales
## 299 299  31       Yes        Non-Travel       335 Research & Development
## 543 543  27        No        Non-Travel      1450 Research & Development
## 214 214  42        No     Travel_Rarely       932 Research & Development
## 366 366  56        No Travel_Frequently       906                  Sales
## 624 624  35        No     Travel_Rarely      1146        Human Resources
## 610 610  33        No        Non-Travel      1038                  Sales
## 250 250  41        No     Travel_Rarely      1276                  Sales
## 119 119  41        No     Travel_Rarely       465 Research & Development
## 103 103  34        No        Non-Travel      1381                  Sales
## 221 221  34        No     Travel_Rarely       304                  Sales
## 630 630  34        No     Travel_Rarely      1442 Research & Development
## 605 605  29       Yes     Travel_Rarely       428                  Sales
## 462 462  36        No     Travel_Rarely       311 Research & Development
## 714 714  36        No Travel_Frequently       541                  Sales
## 517 517  25       Yes     Travel_Rarely       867                  Sales
## 287 287  22       Yes     Travel_Rarely       391 Research & Development
## 519 519  30        No     Travel_Rarely      1138 Research & Development
## 674 674  35       Yes Travel_Frequently       130 Research & Development
## 392 392  26        No     Travel_Rarely      1443                  Sales
## 758 758  49        No     Travel_Rarely      1495 Research & Development
## 514 514  33        No Travel_Frequently       515 Research & Development
## 668 668  33       Yes     Travel_Rarely       587 Research & Development
## 317 317  34        No Travel_Frequently       303                  Sales
## 52   52  58        No     Travel_Rarely      1055 Research & Development
## 511 511  38        No     Travel_Rarely       345                  Sales
## 815 815  38        No     Travel_Rarely       395                  Sales
## 341 341  36        No     Travel_Rarely       852 Research & Development
## 612 612  41        No        Non-Travel       509 Research & Development
## 564 564  31        No        Non-Travel       697 Research & Development
## 183 183  43        No Travel_Frequently       957 Research & Development
## 97   97  46        No     Travel_Rarely       228                  Sales
## 602 602  32        No        Non-Travel      1200 Research & Development
## 711 711  30        No     Travel_Rarely       634 Research & Development
## 586 586  40        No     Travel_Rarely       905 Research & Development
## 642 642  40        No     Travel_Rarely       750 Research & Development
## 651 651  35        No     Travel_Rarely       992 Research & Development
## 666 666  29        No Travel_Frequently       461 Research & Development
## 508 508  51        No Travel_Frequently       541                  Sales
## 70   70  37        No     Travel_Rarely      1372 Research & Development
## 73   73  35       Yes     Travel_Rarely       556 Research & Development
## 509 509  37        No Travel_Frequently       319                  Sales
## 704 704  26       Yes     Travel_Rarely       920        Human Resources
## 455 455  48        No     Travel_Rarely      1236 Research & Development
## 662 662  34        No     Travel_Rarely      1303 Research & Development
## 7     7  41        No     Travel_Rarely      1283 Research & Development
## 198 198  49        No     Travel_Rarely       174                  Sales
## 442 442  29        No     Travel_Rarely      1082 Research & Development
## 756 756  44        No        Non-Travel       381 Research & Development
## 322 322  34        No     Travel_Rarely      1397 Research & Development
## 265 265  24        No     Travel_Rarely      1127 Research & Development
## 150 150  28        No     Travel_Rarely       580 Research & Development
## 232 232  35        No     Travel_Rarely       890                  Sales
## 421 421  34        No     Travel_Rarely      1153 Research & Development
## 231 231  45        No     Travel_Rarely      1329 Research & Development
## 786 786  31        No     Travel_Rarely       182 Research & Development
## 241 241  42        No     Travel_Rarely       319 Research & Development
## 228 228  22        No     Travel_Rarely      1136 Research & Development
## 284 284  31        No     Travel_Rarely       691                  Sales
## 99   99  29        No Travel_Frequently       410 Research & Development
## 181 181  35        No     Travel_Rarely       819 Research & Development
## 496 496  51        No     Travel_Rarely      1318                  Sales
## 39   39  56        No     Travel_Rarely       206        Human Resources
## 212 212  42        No     Travel_Rarely       265                  Sales
## 107 107  29        No        Non-Travel      1496 Research & Development
## 325 325  39        No     Travel_Rarely       466 Research & Development
## 635 635  43        No     Travel_Rarely      1001 Research & Development
## 343 343  33        No     Travel_Rarely       654 Research & Development
## 237 237  42        No     Travel_Rarely       300 Research & Development
## 206 206  44        No     Travel_Rarely       661 Research & Development
## 607 607  59        No Travel_Frequently      1225                  Sales
## 318 318  24        No     Travel_Rarely       771 Research & Development
## 772 772  46        No     Travel_Rarely       526                  Sales
## 645 645  41       Yes        Non-Travel       906 Research & Development
## 749 749  24        No     Travel_Rarely       506 Research & Development
## 385 385  51       Yes     Travel_Rarely      1323 Research & Development
## 794 794  36        No     Travel_Rarely      1223 Research & Development
## 313 313  31       Yes Travel_Frequently       561 Research & Development
## 380 380  30       Yes     Travel_Rarely       138 Research & Development
## 260 260  25        No     Travel_Rarely       977 Research & Development
## 10   10  34        No Travel_Frequently       653 Research & Development
## 447 447  38        No Travel_Frequently      1189 Research & Development
## 534 534  32        No        Non-Travel      1146 Research & Development
## 497 497  45        No     Travel_Rarely       192 Research & Development
## 352 352  31        No     Travel_Rarely       218                  Sales
## 746 746  24        No     Travel_Rarely       350 Research & Development
## 158 158  39       Yes Travel_Frequently       203 Research & Development
## 483 483  34        No Travel_Frequently       735 Research & Development
## 323 323  30        No Travel_Frequently      1012 Research & Development
## 324 324  46        No     Travel_Rarely      1003 Research & Development
## 535 535  29       Yes     Travel_Rarely       318 Research & Development
## 307 307  50        No     Travel_Rarely       804 Research & Development
## 694 694  30       Yes Travel_Frequently       464 Research & Development
## 750 750  33        No     Travel_Rarely       134 Research & Development
## 16   16  31        No        Non-Travel      1188                  Sales
## 855 855  35        No     Travel_Rarely       219 Research & Development
## 823 823  34        No     Travel_Rarely       704                  Sales
## 591 591  22        No        Non-Travel       457 Research & Development
## 360 360  36        No     Travel_Rarely      1403 Research & Development
## 470 470  53        No     Travel_Rarely      1084 Research & Development
## 68   68  23        No     Travel_Rarely       571 Research & Development
## 548 548  40        No Travel_Frequently       720 Research & Development
## 670 670  54        No     Travel_Rarely       685 Research & Development
## 424 424  18        No        Non-Travel      1124 Research & Development
## 121 121  36        No        Non-Travel       427 Research & Development
## 818 818  33        No     Travel_Rarely       213 Research & Development
## 258 258  30        No     Travel_Rarely       855                  Sales
## 696 696  47        No     Travel_Rarely      1454                  Sales
## 812 812  25        No     Travel_Rarely       949 Research & Development
## 479 479  52        No     Travel_Rarely       258 Research & Development
## 490 490  34        No     Travel_Rarely       970 Research & Development
## 867 867  32        No        Non-Travel       976                  Sales
## 471 471  27        No     Travel_Rarely      1354 Research & Development
## 862 862  43        No     Travel_Rarely       531                  Sales
## 824 824  31       Yes Travel_Frequently       874 Research & Development
## 522 522  28       Yes     Travel_Rarely       329 Research & Development
## 401 401  30        No     Travel_Rarely       317 Research & Development
## 353 353  40        No Travel_Frequently       902 Research & Development
## 163 163  33        No     Travel_Rarely      1216                  Sales
## 492 492  50        No     Travel_Rarely      1322 Research & Development
## 820 820  28       Yes     Travel_Rarely      1475                  Sales
## 632 632  34        No     Travel_Rarely       937                  Sales
## 384 384  23        No     Travel_Rarely       541                  Sales
## 644 644  34       Yes        Non-Travel       967 Research & Development
## 792 792  34        No     Travel_Rarely       629 Research & Development
## 525 525  59        No     Travel_Rarely       142 Research & Development
## 677 677  36        No     Travel_Rarely       676 Research & Development
## 807 807  25        No     Travel_Rarely      1372                  Sales
## 825 825  36        No     Travel_Rarely       559 Research & Development
## 551 551  41        No     Travel_Rarely       314        Human Resources
## 328 328  42        No     Travel_Rarely       557 Research & Development
## 356 356  34        No     Travel_Rarely      1326                  Sales
## 77   77  34        No     Travel_Rarely       971                  Sales
## 116 116  37        No        Non-Travel      1252                  Sales
## 450 450  45        No        Non-Travel       336                  Sales
## 701 701  29        No     Travel_Rarely       352        Human Resources
## 443 443  28       Yes     Travel_Rarely       529 Research & Development
## 739 739  35        No     Travel_Rarely      1219                  Sales
## 835 835  55        No        Non-Travel       177 Research & Development
## 160 160  42        No Travel_Frequently       458 Research & Development
## 850 850  26        No Travel_Frequently      1096 Research & Development
## 604 604  29        No     Travel_Rarely      1401 Research & Development
## 165 165  35        No     Travel_Rarely      1232                  Sales
## 606 606  53        No     Travel_Rarely       970 Research & Development
## 524 524  49        No     Travel_Rarely      1245 Research & Development
## 126 126  35        No     Travel_Rarely      1224                  Sales
## 93   93  37       Yes     Travel_Rarely      1373 Research & Development
## 418 418  36        No Travel_Frequently       566 Research & Development
## 64   64  18        No        Non-Travel      1431 Research & Development
## 685 685  28        No     Travel_Rarely       440 Research & Development
## 293 293  27        No     Travel_Rarely       894 Research & Development
## 727 727  37        No Travel_Frequently      1231                  Sales
## 729 729  26       Yes     Travel_Rarely       471 Research & Development
## 860 860  44       Yes     Travel_Rarely       621 Research & Development
## 609 609  26        No     Travel_Rarely       652 Research & Development
## 246 246  29        No     Travel_Rarely      1086 Research & Development
## 375 375  20        No     Travel_Rarely       727                  Sales
## 159 159  43        No Travel_Frequently      1082 Research & Development
## 108 108  55        No     Travel_Rarely       189        Human Resources
## 489 489  48       Yes     Travel_Rarely       626 Research & Development
## 391 391  34        No     Travel_Rarely      1111                  Sales
## 82   82  35        No     Travel_Rarely      1370 Research & Development
## 379 379  29        No     Travel_Rarely       942 Research & Development
## 439 439  50       Yes     Travel_Rarely       869                  Sales
## 681 681  25        No     Travel_Rarely       583                  Sales
## 477 477  37        No     Travel_Rarely       342                  Sales
## 314 314  51        No     Travel_Rarely      1169 Research & Development
## 843 843  52        No Travel_Frequently       890 Research & Development
## 863 863  34       Yes        Non-Travel      1362                  Sales
## 839 839  47        No     Travel_Rarely      1001 Research & Development
## 362 362  29       Yes Travel_Frequently       746                  Sales
## 660 660  36        No Travel_Frequently      1195 Research & Development
## 643 643  55        No        Non-Travel       444 Research & Development
## 310 310  25        No     Travel_Rarely       810                  Sales
## 45   45  23        No     Travel_Rarely       373 Research & Development
## 390 390  21       Yes Travel_Frequently       756                  Sales
## 193 193  46        No     Travel_Rarely      1485 Research & Development
## 494 494  35        No     Travel_Rarely      1029 Research & Development
## 34   34  35        No     Travel_Rarely       982 Research & Development
## 78   78  46        No     Travel_Rarely       563                  Sales
## 344 344  30        No Travel_Frequently       160 Research & Development
## 753 753  50        No Travel_Frequently      1234 Research & Development
## 202 202  32        No     Travel_Rarely       548 Research & Development
## 6     6  27        No Travel_Frequently       294 Research & Development
## 36   36  32        No Travel_Frequently      1311 Research & Development
## 849 849  40       Yes     Travel_Rarely       575                  Sales
## 340 340  27        No     Travel_Rarely      1240 Research & Development
## 132 132  49        No     Travel_Rarely      1313                  Sales
## 373 373  45        No Travel_Frequently      1249 Research & Development
## 747 747  29       Yes     Travel_Rarely       350        Human Resources
## 44   44  42        No     Travel_Rarely      1059 Research & Development
## 267 267  34        No Travel_Frequently       829 Research & Development
## 410 410  43        No     Travel_Rarely       589 Research & Development
## 760 760  45       Yes     Travel_Rarely      1449                  Sales
## 788 788  52       Yes     Travel_Rarely       723 Research & Development
## 596 596  33        No     Travel_Rarely       586                  Sales
## 302 302  35        No Travel_Frequently       853                  Sales
## 731 731  43        No     Travel_Rarely       177 Research & Development
## 56   56  41        No     Travel_Rarely       548 Research & Development
## 454 454  33        No     Travel_Rarely       217                  Sales
## 382 382  26        No     Travel_Rarely      1355        Human Resources
## 647 647  18       Yes        Non-Travel       247 Research & Development
## 445 445  46        No     Travel_Rarely       734 Research & Development
## 804 804  35       Yes     Travel_Rarely      1204                  Sales
## 104 104  23        No     Travel_Rarely       885 Research & Development
## 437 437  28       Yes     Travel_Rarely       890 Research & Development
## 656 656  26        No Travel_Frequently       496 Research & Development
## 679 679  36        No Travel_Frequently       688 Research & Development
## 752 752  31       Yes Travel_Frequently      1060                  Sales
## 426 426  43        No     Travel_Rarely       415                  Sales
## 296 296  35        No     Travel_Rarely      1490 Research & Development
## 420 420  29        No     Travel_Rarely       986 Research & Development
## 830 830  50        No Travel_Frequently      1246        Human Resources
## 32   32  33        No     Travel_Rarely       267 Research & Development
## 726 726  25        No     Travel_Rarely       685 Research & Development
## 166 166  20       Yes     Travel_Rarely       500                  Sales
## 247 247  31        No     Travel_Rarely      1232 Research & Development
## 117 117  25        No     Travel_Rarely      1356                  Sales
## 845 845  34        No     Travel_Rarely       511                  Sales
## 364 364  40        No     Travel_Rarely      1416 Research & Development
## 557 557  54        No     Travel_Rarely       548 Research & Development
## 376 376  50        No Travel_Frequently       333 Research & Development
## 98   98  37        No        Non-Travel      1040 Research & Development
## 502 502  28       Yes     Travel_Rarely       103 Research & Development
## 736 736  27        No     Travel_Rarely       511                  Sales
## 180 180  38        No     Travel_Rarely       702                  Sales
## 338 338  34        No Travel_Frequently       618 Research & Development
## 501 501  58       Yes     Travel_Rarely       601 Research & Development
## 778 778  32        No        Non-Travel       862                  Sales
## 531 531  38        No     Travel_Rarely       330 Research & Development
## 744 744  23        No     Travel_Rarely       160 Research & Development
## 196 196  51        No     Travel_Rarely       833 Research & Development
## 30   30  52        No        Non-Travel       715 Research & Development
## 31   31  32        No Travel_Frequently       689                  Sales
## 400 400  44        No Travel_Frequently      1193 Research & Development
## 95   95  50        No     Travel_Rarely      1452 Research & Development
## 49   49  18       Yes Travel_Frequently      1306                  Sales
## 771 771  33        No Travel_Frequently       508                  Sales
## 659 659  41        No     Travel_Rarely       483 Research & Development
## 465 465  43       Yes Travel_Frequently       807 Research & Development
## 698 698  36        No     Travel_Rarely      1396 Research & Development
## 798 798  22       Yes     Travel_Rarely      1294 Research & Development
## 782 782  39        No     Travel_Rarely       408 Research & Development
## 291 291  45        No     Travel_Rarely       974 Research & Development
## 764 764  50        No        Non-Travel       145                  Sales
## 211 211  41        No     Travel_Rarely       582 Research & Development
## 634 634  36        No     Travel_Rarely       922 Research & Development
## 654 654  48        No     Travel_Rarely       530                  Sales
## 775 775  42        No Travel_Frequently       288 Research & Development
## 559 559  42        No     Travel_Rarely       933 Research & Development
## 805 805  44        No        Non-Travel       489 Research & Development
## 178 178  26       Yes Travel_Frequently       342 Research & Development
## 110 110  32        No     Travel_Rarely       529 Research & Development
## 857 857  30       Yes     Travel_Rarely      1005 Research & Development
## 337 337  35        No Travel_Frequently      1182                  Sales
## 115 115  35        No     Travel_Rarely      1361                  Sales
## 585 585  44        No     Travel_Rarely      1459 Research & Development
## 460 460  36        No     Travel_Rarely       172 Research & Development
## 505 505  47        No     Travel_Rarely       359 Research & Development
## 576 576  37        No     Travel_Rarely      1115 Research & Development
## 799 799  19       Yes        Non-Travel       504 Research & Development
## 856 856  35        No     Travel_Rarely       660                  Sales
## 233 233  47       Yes Travel_Frequently      1093                  Sales
## 780 780  29       Yes     Travel_Rarely      1092 Research & Development
## 129 129  43        No     Travel_Rarely       930 Research & Development
## 837 837  37        No     Travel_Rarely      1439 Research & Development
## 766 766  45        No     Travel_Rarely      1015 Research & Development
## 569 569  42        No     Travel_Rarely       462                  Sales
## 388 388  29        No     Travel_Rarely       232 Research & Development
## 321 321  32        No        Non-Travel      1184 Research & Development
## 308 308  34        No        Non-Travel       697 Research & Development
## 829 829  38        No Travel_Frequently       594 Research & Development
## 94   94  35        No     Travel_Rarely       670 Research & Development
## 289 289  36        No Travel_Frequently      1467                  Sales
## 408 408  36        No     Travel_Rarely       917 Research & Development
## 368 368  40        No     Travel_Rarely      1342                  Sales
## 164 164  42        No     Travel_Rarely       916 Research & Development
## 413 413  37        No     Travel_Rarely       408 Research & Development
## 487 487  30       Yes     Travel_Rarely       945                  Sales
## 767 767  41        No     Travel_Rarely       509 Research & Development
## 733 733  27       Yes Travel_Frequently      1337        Human Resources
## 735 735  34        No     Travel_Rarely       479 Research & Development
## 808 808  22        No     Travel_Rarely      1256 Research & Development
## 458 458  51        No     Travel_Rarely       632                  Sales
## 552 552  28        No     Travel_Rarely      1169        Human Resources
## 53   53  44        No     Travel_Rarely      1117 Research & Development
## 409 409  53        No     Travel_Rarely      1219                  Sales
## 702 702  34        No     Travel_Rarely      1400                  Sales
## 280 280  55        No     Travel_Rarely      1229 Research & Development
## 327 327  44        No     Travel_Rarely      1313 Research & Development
## 415 415  34       Yes Travel_Frequently       296                  Sales
## 83   83  38        No Travel_Frequently       508 Research & Development
## 396 396  31        No     Travel_Rarely       192 Research & Development
## 133 133  42        No Travel_Frequently      1368 Research & Development
## 275 275  55        No     Travel_Rarely       692 Research & Development
## 456 456  40        No     Travel_Rarely       611                  Sales
## 518 518  42        No     Travel_Rarely       855 Research & Development
## 304 304  35        No     Travel_Rarely      1395 Research & Development
## 238 238  32        No     Travel_Rarely       646 Research & Development
## 507 507  33       Yes     Travel_Rarely       350                  Sales
## 482 482  31        No     Travel_Rarely      1222 Research & Development
## 476 476  46        No Travel_Frequently      1211                  Sales
## 434 434  29        No     Travel_Rarely       590 Research & Development
## 495 495  36        No     Travel_Rarely      1266                  Sales
## 724 724  36        No     Travel_Rarely       796 Research & Development
## 47   47  32       Yes     Travel_Rarely       964                  Sales
## 147 147  33        No     Travel_Rarely       931 Research & Development
## 331 331  49        No     Travel_Rarely       809 Research & Development
## 218 218  48        No Travel_Frequently       117 Research & Development
## 555 555  40        No        Non-Travel      1151 Research & Development
## 201 201  40       Yes        Non-Travel      1479                  Sales
## 587 587  38        No        Non-Travel       573 Research & Development
## 290 290  35        No     Travel_Rarely       384                  Sales
## 467 467  36        No     Travel_Rarely      1157                  Sales
## 393 393  31        No        Non-Travel       976 Research & Development
## 255 255  40        No        Non-Travel       218 Research & Development
## 558 558  34        No     Travel_Rarely       254 Research & Development
## 300 300  28       Yes     Travel_Rarely      1157 Research & Development
## 253 253  45        No     Travel_Rarely       252 Research & Development
## 592 592  34       Yes     Travel_Rarely       790                  Sales
## 59   59  37        No        Non-Travel       728 Research & Development
## 811 811  30        No        Non-Travel       111 Research & Development
## 278 278  43        No Travel_Frequently       185 Research & Development
## 137 137  29       Yes     Travel_Rarely       805 Research & Development
## 225 225  38        No     Travel_Rarely      1206 Research & Development
## 191 191  30        No     Travel_Rarely      1275 Research & Development
## 544 544  38        No     Travel_Rarely      1261 Research & Development
## 473 473  45        No     Travel_Rarely       193 Research & Development
## 523 523  45        No     Travel_Rarely      1339 Research & Development
## 710 710  50        No        Non-Travel       881 Research & Development
## 861 861  51        No Travel_Frequently       968 Research & Development
## 182 182  38        No     Travel_Rarely      1009                  Sales
## 852 852  30        No     Travel_Rarely       125 Research & Development
## 236 236  37       Yes     Travel_Rarely       625                  Sales
## 779 779  26        No        Non-Travel       786 Research & Development
## 187 187  25        No Travel_Frequently       772 Research & Development
## 717 717  30        No     Travel_Rarely       852                  Sales
## 740 740  35        No     Travel_Rarely       538 Research & Development
## 629 629  55        No Travel_Frequently      1091 Research & Development
## 311 311  34        No     Travel_Rarely      1351 Research & Development
## 399 399  56        No        Non-Travel       667 Research & Development
## 131 131  29        No     Travel_Rarely       991                  Sales
## 486 486  41       Yes     Travel_Rarely      1102                  Sales
## 370 370  32        No     Travel_Rarely      1018 Research & Development
## 199 199  44        No     Travel_Rarely       477 Research & Development
## 783 783  33        No     Travel_Rarely      1075        Human Resources
## 566 566  44        No     Travel_Rarely      1037 Research & Development
## 226 226  30        No     Travel_Rarely       911 Research & Development
## 488 488  31        No     Travel_Rarely       670 Research & Development
## 614 614  19       Yes     Travel_Rarely       419                  Sales
## 320 320  19        No     Travel_Rarely       265 Research & Development
## 800 800  36        No     Travel_Rarely      1040 Research & Development
## 700 700  46        No     Travel_Rarely      1450 Research & Development
## 762 762  47        No     Travel_Rarely       202 Research & Development
## 684 684  57        No     Travel_Rarely       334 Research & Development
## 220 220  26        No     Travel_Rarely       583 Research & Development
## 229 229  24        No     Travel_Rarely      1353                  Sales
## 11   11  43        No     Travel_Rarely       823 Research & Development
## 342 342  28        No Travel_Frequently       193 Research & Development
## 716 716  24       Yes     Travel_Rarely       240        Human Resources
## 65   65  24       Yes     Travel_Rarely       984 Research & Development
## 259 259  34        No     Travel_Rarely      1480                  Sales
## 540 540  42        No     Travel_Rarely       647                  Sales
## 24   24  31        No Travel_Frequently       715                  Sales
## 134 134  39        No     Travel_Rarely       492 Research & Development
## 590 590  35        No     Travel_Rarely      1142 Research & Development
## 286 286  33        No     Travel_Rarely       589 Research & Development
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## 249 249  41        No Travel_Frequently      1200 Research & Development
## 529 529  35        No     Travel_Rarely      1214 Research & Development
## 215 215  27        No     Travel_Rarely      1115 Research & Development
## 449 449  38        No Travel_Frequently       653 Research & Development
## 20   20  44        No     Travel_Rarely       170 Research & Development
## 530 530  56       Yes     Travel_Rarely       441 Research & Development
## 294 294  47        No        Non-Travel      1162 Research & Development
## 89   89  27        No Travel_Frequently       994                  Sales
## 435 435  40        No     Travel_Rarely       658                  Sales
## 436 436  27        No     Travel_Rarely      1130                  Sales
## 330 330  42        No     Travel_Rarely      1147        Human Resources
## 639 639  24        No     Travel_Rarely       691 Research & Development
## 663 663  46        No     Travel_Rarely      1277                  Sales
## 469 469  26       Yes        Non-Travel       265                  Sales
## 868 868  47        No     Travel_Rarely       571                  Sales
## 713 713  48        No     Travel_Rarely       969 Research & Development
## 594 594  49        No     Travel_Rarely       722 Research & Development
## 579 579  39        No Travel_Frequently       711 Research & Development
## 646 646  40        No     Travel_Rarely       630                  Sales
## 633 633  38        No     Travel_Rarely      1035                  Sales
## 581 581  25        No     Travel_Rarely       882 Research & Development
## 513 513  32        No Travel_Frequently       585 Research & Development
## 619 619  39        No     Travel_Rarely       613 Research & Development
## 282 282  41        No     Travel_Rarely       263 Research & Development
## 791 791  49        No     Travel_Rarely       271 Research & Development
## 699 699  18       Yes Travel_Frequently       544                  Sales
## 601 601  24        No        Non-Travel       830                  Sales
## 210 210  41        No     Travel_Rarely       802                  Sales
## 172 172  47        No        Non-Travel      1169 Research & Development
## 22   22  48        No     Travel_Rarely       817                  Sales
## 453 453  35       Yes     Travel_Rarely       303                  Sales
## 776 776  23        No     Travel_Rarely       310 Research & Development
## 626 626  27        No     Travel_Rarely      1167 Research & Development
## 252 252  36        No        Non-Travel      1229                  Sales
## 480 480  37        No     Travel_Rarely       161 Research & Development
## 648 648  26        No     Travel_Rarely       390 Research & Development
## 840 840  48        No     Travel_Rarely      1224 Research & Development
## 814 814  47        No     Travel_Rarely      1176        Human Resources
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## 438 438  26        No     Travel_Rarely       157 Research & Development
## 279 279  25       Yes     Travel_Rarely       240                  Sales
## 203 203  32        No     Travel_Rarely       859 Research & Development
## 405 405  35        No     Travel_Rarely      1343 Research & Development
## 138 138  54        No     Travel_Rarely      1441 Research & Development
## 217 217  38        No     Travel_Rarely      1495 Research & Development
## 854 854  28        No     Travel_Rarely      1476 Research & Development
##     DistanceFromHome Education   EducationField EmployeeCount
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## 224              2        1        Research Scientist               3
## 580              3        2 Healthcare Representative               4
## 622              4        2    Manufacturing Director               3
## 155              3        1     Laboratory Technician               2
## 847              3        1     Laboratory Technician               2
## 542              4        1        Research Scientist               1
## 395              3        1        Research Scientist               4
## 67               1        2        Research Scientist               4
## 25               2        2 Healthcare Representative               4
## 288              3        1     Laboratory Technician               4
## 358              2        1        Research Scientist               2
## 827              3        3           Sales Executive               3
## 728              3        1        Research Scientist               3
## 730              3        1     Laboratory Technician               4
## 303              2        1        Research Scientist               2
## 69               3        1        Research Scientist               1
## 264              3        1      Sales Representative               4
## 562              3        4         Research Director               3
## 675              4        1        Research Scientist               3
## 427              3        1        Research Scientist               3
## 223              3        2           Sales Executive               4
## 35               2        2    Manufacturing Director               4
## 533              4        1        Research Scientist               3
## 239              3        1           Human Resources               2
## 243              3        1        Research Scientist               2
## 707              3        2        Research Scientist               4
## 43               3        1     Laboratory Technician               1
## 777              3        1           Human Resources               3
## 419              2        2    Manufacturing Director               4
## 333              3        1        Research Scientist               2
## 640              3        1      Sales Representative               1
## 757              3        2 Healthcare Representative               4
## 774              2        1        Research Scientist               3
## 864              3        4           Sales Executive               3
## 499              3        2 Healthcare Representative               3
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## 84               4        2        Research Scientist               1
## 761              2        1        Research Scientist               1
## 37               2        3           Sales Executive               1
## 292              3        2     Laboratory Technician               2
## 537              3        3           Sales Executive               2
## 118              2        2     Laboratory Technician               2
## 261              2        1      Sales Representative               4
## 213              4        1        Research Scientist               4
## 459              3        1        Research Scientist               1
## 593              3        1      Sales Representative               3
## 448              4        2 Healthcare Representative               1
## 26               3        5                   Manager               2
## 599              4        2    Manufacturing Director               2
## 113              3        3 Healthcare Representative               2
## 345              3        2           Sales Executive               1
## 870              3        2     Laboratory Technician               2
## 177              3        5                   Manager               1
## 156              3        2           Sales Executive               3
## 568              2        2           Sales Executive               1
## 673              3        2           Sales Executive               1
## 432              3        1        Research Scientist               3
## 759              3        1     Laboratory Technician               3
## 440              4        2 Healthcare Representative               4
## 347              3        2    Manufacturing Director               3
## 833              2        1     Laboratory Technician               1
## 274              2        2           Sales Executive               1
## 40               4        1      Sales Representative               4
## 636              2        1     Laboratory Technician               4
## 2                2        5         Research Director               3
## 216              3        1        Research Scientist               3
## 312              3        4    Manufacturing Director               3
## 305              3        1        Research Scientist               3
## 838              4        2           Sales Executive               4
## 686              3        3    Manufacturing Director               2
## 162              2        3 Healthcare Representative               1
## 866              3        3    Manufacturing Director               4
## 832              3        1      Sales Representative               3
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## 329              2        2        Research Scientist               2
## 179              3        3           Sales Executive               4
## 545              2        1        Research Scientist               1
## 397              3        4         Research Director               3
## 185              3        1     Laboratory Technician               3
## 357              2        1        Research Scientist               2
## 170              3        1        Research Scientist               4
## 251              2        1     Laboratory Technician               3
## 638              3        3           Sales Executive               3
## 869              3        1        Research Scientist               3
## 429              2        1     Laboratory Technician               3
## 57               4        1        Research Scientist               3
## 705              3        1           Human Resources               3
## 55               3        2    Manufacturing Director               4
## 853              2        2     Laboratory Technician               3
## 478              3        2           Sales Executive               3
## 797              3        4 Healthcare Representative               4
## 770              2        2           Sales Executive               3
## 257              3        1      Sales Representative               2
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## 66               4        1        Research Scientist               2
## 687              3        3 Healthcare Representative               4
## 339              3        2           Sales Executive               3
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## 676              2        5                   Manager               2
## 143              3        3    Manufacturing Director               3
## 561              3        1     Laboratory Technician               2
## 254              4        1           Human Resources               1
## 189              3        5         Research Director               2
## 1                3        2           Sales Executive               4
## 858              2        3    Manufacturing Director               3
## 578              2        1     Laboratory Technician               3
## 234              3        2           Sales Executive               1
## 751              3        1      Sales Representative               4
## 299              2        1        Research Scientist               1
## 543              2        1        Research Scientist               3
## 214              2        2    Manufacturing Director               4
## 366              4        4           Sales Executive               1
## 624              3        3           Human Resources               4
## 610              2        1      Sales Representative               4
## 250              3        4                   Manager               1
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## 221              3        2           Sales Executive               4
## 630              2        3 Healthcare Representative               2
## 605              1        1      Sales Representative               2
## 462              3        1     Laboratory Technician               2
## 714              2        3           Sales Executive               4
## 517              2        1      Sales Representative               2
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## 674              3        1        Research Scientist               2
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## 758              3        2 Healthcare Representative               3
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## 668              1        1     Laboratory Technician               4
## 317              3        1      Sales Representative               3
## 52               3        5         Research Director               1
## 511              3        2           Sales Executive               4
## 815              2        1      Sales Representative               2
## 341              2        1        Research Scientist               1
## 612              2        2 Healthcare Representative               3
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## 602              3        2        Research Scientist               1
## 711              3        3                   Manager               1
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## 642              3        2 Healthcare Representative               1
## 651              2        1     Laboratory Technician               1
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## 70               3        1        Research Scientist               4
## 73               2        2    Manufacturing Director               3
## 509              3        1      Sales Representative               4
## 704              3        1           Human Resources               2
## 455              2        4                   Manager               1
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## 7                4        1        Research Scientist               3
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## 756              1        1     Laboratory Technician               3
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## 231              2        2    Manufacturing Director               4
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## 241              3        3    Manufacturing Director               1
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## 39               3        5                   Manager               2
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## 380              3        1        Research Scientist               3
## 260              3        1     Laboratory Technician               3
## 10               2        2 Healthcare Representative               3
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## 534              3        2 Healthcare Representative               4
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## 746              2        1     Laboratory Technician               1
## 158              3        4 Healthcare Representative               4
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## 323              2        1        Research Scientist               4
## 324              2        2        Research Scientist               1
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## 694              3        1        Research Scientist               4
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## 855              2        2    Manufacturing Director               2
## 823              2        2           Sales Executive               3
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## 470              4        2    Manufacturing Director               1
## 68               3        1     Laboratory Technician               4
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## 424              3        1     Laboratory Technician               4
## 121              4        3         Research Director               1
## 818              3        3         Research Director               3
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## 696              2        1      Sales Representative               4
## 812              3        1     Laboratory Technician               4
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## 471              3        1        Research Scientist               2
## 862              2        3           Sales Executive               4
## 824              3        1     Laboratory Technician               3
## 522              3        1     Laboratory Technician               2
## 401              1        2    Manufacturing Director               4
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## 632              3        3           Sales Executive               4
## 384              3        1      Sales Representative               1
## 644              1        1        Research Scientist               1
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## 525              2        1        Research Scientist               4
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## 450              2        2           Sales Executive               1
## 701              2        1           Human Resources               2
## 443              3        1     Laboratory Technician               3
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## 835              2        4 Healthcare Representative               2
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## 293              3        1        Research Scientist               2
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## 246              2        1     Laboratory Technician               4
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## 56               3        1     Laboratory Technician               1
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## 300              1        1        Research Scientist               4
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## 137              2        1     Laboratory Technician               1
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## 24               3        2           Sales Executive               1
## 134              3        2    Manufacturing Director               2
## 590              3        1     Laboratory Technician               1
## 286              3        2     Laboratory Technician               3
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## 469              1        2           Sales Executive               1
## 868              3        2           Sales Executive               3
## 713              4        1     Laboratory Technician               2
## 594              3        1     Laboratory Technician               1
## 579              3        2    Manufacturing Director               3
## 646              2        3           Sales Executive               4
## 633              3        2           Sales Executive               4
## 581              3        1     Laboratory Technician               4
## 513              3        1        Research Scientist               3
## 619              2        3 Healthcare Representative               1
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## 791              2        2     Laboratory Technician               1
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## 22               4        2           Sales Executive               2
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## 626              3        1        Research Scientist               3
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## 648              1        1     Laboratory Technician               3
## 840              2        5         Research Director               2
## 814              3        5                   Manager               3
## 285              4        1           Human Resources               3
## 438              3        1     Laboratory Technician               1
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## 203              2        2    Manufacturing Director               3
## 405              2        1        Research Scientist               1
## 138              3        3    Manufacturing Director               3
## 217              3        2 Healthcare Representative               3
## 854              4        2 Healthcare Representative               1
##     MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## 834        Single          2552        7172                  1      Y
## 697       Married         15202        5602                  2      Y
## 207        Single          5346        9489                  8      Y
## 715        Single          7587       14229                  1      Y
## 809       Married          2862        3811                  1      Y
## 725        Single          3968       13624                  4      Y
## 222       Married          4286        5630                  2      Y
## 71        Married          2782       21412                  3      Y
## 503       Married          2655       11740                  2      Y
## 403        Single          2105        5411                  4      Y
## 387        Single          8789        9096                  1      Y
## 806       Married          8321       25949                  7      Y
## 826      Divorced         19627       21445                  9      Y
## 821       Married          3312       18783                  3      Y
## 844      Divorced          8500        5494                  0      Y
## 528       Married          6833       17089                  1      Y
## 128       Married          5906       23888                  0      Y
## 539        Single          3202       21972                  1      Y
## 457       Married          5476       22589                  1      Y
## 732       Married          5238       17778                  4      Y
## 842        Single          7988        9769                  1      Y
## 85         Single          5410        2323                  9      Y
## 122        Single          2380       25479                  1      Y
## 474        Single          3452        9752                  5      Y
## 819        Single          2720       11162                  0      Y
## 625        Single          2561        5355                  7      Y
## 184        Single          8376        9150                  4      Y
## 92        Married          7336       11162                  1      Y
## 269      Divorced          5968       18079                  1      Y
## 463        Single          9208        6645                  4      Y
## 407       Married          5468       13402                  1      Y
## 235       Married         16959       19494                  1      Y
## 54        Married          4447       23163                  1      Y
## 146       Married          4649       16928                  1      Y
## 464        Single          5238        6670                  2      Y
## 192       Married          9637        8277                  2      Y
## 665        Single          4568       10034                  0      Y
## 394       Married         10266        2845                  4      Y
## 810       Married          3058       13364                  0      Y
## 512       Married         10920        3449                  3      Y
## 628      Divorced         11510       15682                  0      Y
## 248       Married          2269       18024                  0      Y
## 8        Divorced          6694       24223                  2      Y
## 468       Married          3038       22002                  3      Y
## 851       Married          4559       24788                  3      Y
## 404       Married          8008       22792                  4      Y
## 365       Married         19187        6992                  4      Y
## 491        Single         18665       25594                  9      Y
## 616       Married          3986       11912                  1      Y
## 748        Single          5343       25755                  0      Y
## 123      Divorced         10761       19239                  4      Y
## 841        Single          1420       25233                  1      Y
## 326        Single          2318       17808                  1      Y
## 149        Single          3597        6409                  8      Y
## 91        Married          1702       12106                  1      Y
## 72         Single          8463       23490                  0      Y
## 563       Married          2811       12086                  9      Y
## 80       Divorced          1129       17536                  1      Y
## 828        Single          4538        6039                  0      Y
## 481       Married          5373        6225                  0      Y
## 466        Single          3944        4306                  5      Y
## 510       Married         19545       16280                  1      Y
## 381       Married         18300       16375                  4      Y
## 90         Single         18947       22822                  3      Y
## 127        Single          3919        4681                  1      Y
## 737       Married         12031        8828                  0      Y
## 527      Divorced          5006        6319                  4      Y
## 100       Married          3294        3708                  5      Y
## 209      Divorced          3204       10415                  5      Y
## 148       Married          6392       10589                  2      Y
## 125        Single          4554       20260                  1      Y
## 754       Married          7314       14011                  5      Y
## 139       Married          2404        4303                  7      Y
## 649        Single          4312       23016                  0      Y
## 516      Divorced          5482       16321                  5      Y
## 3          Single          9362       19944                  2      Y
## 412        Single          9241       15869                  1      Y
## 667       Married          7918        6599                  1      Y
## 227      Divorced          1951       10910                  1      Y
## 4         Married         10422       24032                  1      Y
## 27        Married          9924       12355                  0      Y
## 240      Divorced         13269       21981                  5      Y
## 169      Divorced          5577       22087                  3      Y
## 422       Married          4449       23866                  3      Y
## 130      Divorced         13225        7739                  2      Y
## 784       Married          4539        4905                  1      Y
## 188      Divorced         15972       21086                  6      Y
## 521        Single          2553        8306                  1      Y
## 745      Divorced          4907       13684                  0      Y
## 256        Single          2438       24978                  4      Y
## 145      Divorced          6377       13888                  5      Y
## 262        Single          2362        7568                  6      Y
## 369       Married          2073       12826                  2      Y
## 573       Married          6834       19255                  1      Y
## 135      Divorced          5257        6227                  1      Y
## 152        Single          5396       21703                  1      Y
## 374       Married          3936        9953                  1      Y
## 822      Divorced          6931       10732                  2      Y
## 785       Married          4978        3536                  7      Y
## 446       Married          3420       10205                  7      Y
## 793       Married          7756       14199                  3      Y
## 17        Married          6725       13554                  1      Y
## 718        Single          3660        7909                  3      Y
## 176        Single          2070       25326                  1      Y
## 319       Married          6120        3567                  3      Y
## 372       Married          8412        2890                  0      Y
## 461       Married          2093        9260                  4      Y
## 361       Married         19999        5678                  0      Y
## 141       Married         13402       18235                  4      Y
## 28         Single          8224       18385                  0      Y
## 423       Married          6172       20739                  4      Y
## 363       Married         19845       25846                  1      Y
## 796        Single          3348        3164                  1      Y
## 859       Married          2238        6961                  2      Y
## 295        Single         16437       17381                  1      Y
## 574       Married          6799       22128                  1      Y
## 703       Married         10377       13755                  4      Y
## 334       Married          7412        6009                  1      Y
## 268        Single          1393       24852                  1      Y
## 272      Divorced          5433       19332                  1      Y
## 671        Single         13664       25258                  4      Y
## 51         Single          2619       14561                  3      Y
## 153       Married          5484       16985                  1      Y
## 441        Single          4998        2338                  4      Y
## 154        Single          6870       15530                  3      Y
## 87         Single          4960       11825                  2      Y
## 575       Married          6272        7428                  1      Y
## 414        Single          4648       26075                  8      Y
## 763        Single          6151       22074                  1      Y
## 58         Single          4723       16213                  1      Y
## 224      Divorced          1563       12530                  1      Y
## 580      Divorced          6623        4204                  1      Y
## 622      Divorced          5067        6759                  1      Y
## 155      Divorced          2008        6896                  1      Y
## 847        Single          1483       16102                  1      Y
## 542       Married          3143        6076                  6      Y
## 395        Single          2515        9068                  5      Y
## 67         Single          6646       19368                  1      Y
## 25         Single          6949       12291                  0      Y
## 288      Divorced          2661        8758                  0      Y
## 358       Married          2546       18300                  5      Y
## 827       Married          8161       19002                  2      Y
## 728      Divorced          3815        5972                  1      Y
## 730       Married          2523       19299                  0      Y
## 303      Divorced          3537       23737                  5      Y
## 69        Married          2194        5868                  4      Y
## 264       Married          2157       18203                  1      Y
## 562       Married         15787       21624                  2      Y
## 675       Married          3517       22490                  7      Y
## 427      Divorced          1274        7152                  1      Y
## 223      Divorced          5368       16130                  1      Y
## 35         Single          9679       10138                  8      Y
## 533       Married          2700       23779                  1      Y
## 239       Married          2187       19655                  4      Y
## 243       Married          2703        4956                  0      Y
## 707       Married          6854       15696                  4      Y
## 43       Divorced          2587       24941                  4      Y
## 777      Divorced          2064       15428                  0      Y
## 419       Married          6474        9961                  1      Y
## 333      Divorced          2342       11092                  1      Y
## 640      Divorced          3041       16346                  0      Y
## 757       Married          4014       19170                  1      Y
## 774       Married          3388       21777                  0      Y
## 864      Divorced         13770       10225                  9      Y
## 499       Married          5237       16577                  6      Y
## 484      Divorced          4257       13939                  4      Y
## 281       Married          7847        6069                  1      Y
## 84       Divorced          5974       17001                  4      Y
## 761        Single          2404       11479                  6      Y
## 37        Married         10306       21530                  9      Y
## 292       Married          5674        6927                  7      Y
## 537        Single          7351       20619                  7      Y
## 118       Married          4025       23679                  4      Y
## 261        Single          2785       11882                  7      Y
## 213        Single          4680       15232                  3      Y
## 459       Married          2066       10494                  2      Y
## 593       Married          2909       15747                  3      Y
## 448       Married          5538        5696                  5      Y
## 26        Married         18213        8751                  7      Y
## 599       Married          6142        5174                  1      Y
## 113        Single         10445       15322                  7      Y
## 345      Divorced          4115       13192                  8      Y
## 870        Single          4425       15986                  5      Y
## 177       Married         19859       21199                  5      Y
## 156       Married          4736        6069                  7      Y
## 568       Married          9714        5323                  1      Y
## 673        Single          4898       11827                  0      Y
## 432      Divorced          3544        8532                  9      Y
## 759       Married          3306       26176                  7      Y
## 440        Single          7625       19383                  0      Y
## 347        Single          4033       15834                  2      Y
## 833       Married          3196       12449                  1      Y
## 274       Married          5647       13494                  4      Y
## 40        Married          2791       21981                  0      Y
## 636       Married          2376       26537                  1      Y
## 2          Single         19626       17544                  1      Y
## 216      Divorced          2345        8045                  2      Y
## 312        Single         13237       20364                  7      Y
## 305        Single          2479       26227                  4      Y
## 838        Single          5337       19921                  1      Y
## 686      Divorced         10648       14394                  1      Y
## 162        Single          8722       12355                  1      Y
## 866        Single          7898       18706                  1      Y
## 832        Single          1675       26820                  1      Y
## 627       Married          4162       15211                  1      Y
## 329      Divorced          2133       18115                  1      Y
## 179        Single          8998       15589                  1      Y
## 545        Single          4381        7530                  1      Y
## 397       Married         17861        2288                  6      Y
## 185       Married          3755       17872                  1      Y
## 357       Married          2368       23300                  1      Y
## 170      Divorced          2514       26968                  4      Y
## 251      Divorced          2008       20439                  1      Y
## 638      Divorced          8628       22914                  1      Y
## 869       Married          4477       20100                  4      Y
## 429        Single          3730        9571                  0      Y
## 57       Divorced          2759       20366                  6      Y
## 705       Married          2267       25657                  8      Y
## 55         Single          4424       20682                  1      Y
## 853        Single          4193       12682                  0      Y
## 478      Divorced          4306        4267                  5      Y
## 797       Married         13496        7501                  0      Y
## 770       Married          5593       17970                  1      Y
## 257      Divorced          2789        3909                  1      Y
## 79         Single          8865       16840                  6      Y
## 66       Divorced          3298       15053                  0      Y
## 687        Single          7553       22930                  0      Y
## 339        Single          4728       17251                  3      Y
## 631       Married          2662        7975                  8      Y
## 676       Married         18789        9946                  2      Y
## 143      Divorced         10845       24208                  6      Y
## 561        Single          2088       15062                  4      Y
## 254       Married          2956       21495                  0      Y
## 189      Divorced         19144       15815                  3      Y
## 1        Divorced          4403        9250                  2      Y
## 858       Married          8943       14034                  1      Y
## 578        Single          3162        7973                  3      Y
## 234       Married          5940       17011                  2      Y
## 751        Single          2323       17205                  1      Y
## 299        Single          2321       10322                  0      Y
## 543      Divorced          2566       25326                  1      Y
## 214       Married          6062        4051                  9      Y
## 366       Married         13212       18256                  9      Y
## 624        Single          8837       16642                  1      Y
## 610        Single          2342       21437                  0      Y
## 250       Married         16595        5626                  7      Y
## 119      Divorced          2451        4609                  4      Y
## 103       Married          6538       12740                  9      Y
## 221        Single          6274       18686                  1      Y
## 630        Single          8621       17654                  1      Y
## 605        Single          2760       14630                  1      Y
## 462        Single          2013       10950                  2      Y
## 714       Married          9699        7246                  4      Y
## 517       Married          2413       18798                  1      Y
## 287        Single          2472       26092                  1      Y
## 519       Married          4627       23631                  0      Y
## 674      Divorced          2022       16612                  1      Y
## 392       Married          4157       21436                  7      Y
## 758       Married          6651       21534                  2      Y
## 514        Single         13458       15146                  1      Y
## 668      Divorced          3408        6705                  7      Y
## 317       Married          2231       11314                  6      Y
## 52        Married         19701       22456                  3      Y
## 511       Married          5343        5982                  1      Y
## 815       Married          2899       12102                  0      Y
## 341       Married          3419       13072                  9      Y
## 612        Single          6811        2112                  2      Y
## 564       Married         11031       26862                  4      Y
## 183        Single          4739       16090                  4      Y
## 97        Married         16606       11380                  8      Y
## 602       Married          4087       25174                  4      Y
## 711       Married         11916       25927                  1      Y
## 586       Married          2741       16523                  8      Y
## 642      Divorced          4448       10748                  2      Y
## 651        Single          2450       21731                  1      Y
## 666        Single          6294       23060                  8      Y
## 508       Married         10596       15395                  2      Y
## 70         Single          2115       15881                  1      Y
## 73        Married          5916       15497                  3      Y
## 509      Divorced          2793        2539                  4      Y
## 704       Married          2148        6889                  0      Y
## 455       Married         15402       17997                  7      Y
## 662      Divorced          2768        8416                  3      Y
## 7         Married          2127        5561                  2      Y
## 198       Married         13120       11879                  6      Y
## 442       Married          2974       25412                  9      Y
## 756        Single          3708        2104                  2      Y
## 322       Married          2691        7660                  1      Y
## 265       Married          2774       13257                  0      Y
## 150      Divorced          4877       20460                  0      Y
## 232       Married          2014        9687                  1      Y
## 421       Married          4325       17736                  1      Y
## 231      Divorced          5770        5388                  1      Y
## 786        Single         16422        8847                  3      Y
## 241       Married          7406        6950                  1      Y
## 228      Divorced          2328       12392                  1      Y
## 284      Divorced          7547        7143                  4      Y
## 99        Married          3180        4668                  0      Y
## 181      Divorced         10274       19588                  2      Y
## 496       Married         16307        5594                  2      Y
## 39         Single         19717        4022                  6      Y
## 212       Married         18303        7770                  6      Y
## 107       Married          4319       26283                  1      Y
## 325       Married         12742        7060                  1      Y
## 635       Married          9985        9262                  8      Y
## 343      Divorced          7119       21214                  4      Y
## 237       Married         18880       17312                  5      Y
## 206       Married          2559        7508                  1      Y
## 607        Single          5473       24668                  7      Y
## 318        Single          4617       14120                  1      Y
## 772      Divorced         10453        2137                  1      Y
## 645      Divorced          2107       20293                  6      Y
## 749      Divorced          3907        3622                  1      Y
## 385       Married          2461       10332                  9      Y
## 794      Divorced         10096        8202                  1      Y
## 313        Single          4084        4156                  1      Y
## 380       Married          2132       11539                  4      Y
## 260      Divorced          3977        7298                  6      Y
## 10        Married          5063       15332                  1      Y
## 447       Married          4735        9867                  7      Y
## 534      Divorced          6667       16542                  5      Y
## 497       Married          2654        9655                  3      Y
## 352       Married          6929       12241                  4      Y
## 746      Divorced          2296       10036                  0      Y
## 158      Divorced         12169       13547                  7      Y
## 483       Married          5747       26496                  1      Y
## 323      Divorced          3761        2373                  9      Y
## 324      Divorced          4615       21029                  8      Y
## 535       Married          2119        4759                  1      Y
## 307       Married          2380       20165                  4      Y
## 694        Single          2285        3427                  9      Y
## 750        Single          2500       10515                  0      Y
## 16        Married          6932       24406                  1      Y
## 855       Married          4788       25388                  0      Y
## 823       Married          6712        8978                  1      Y
## 591       Married          2814       10293                  1      Y
## 360       Married          3210       20251                  0      Y
## 470      Divorced          4450       26250                  1      Y
## 68         Single          2647       13672                  1      Y
## 548        Single          5094       11983                  6      Y
## 670       Married         17779       23474                  3      Y
## 424        Single          1611       19305                  1      Y
## 121       Married         11713       20335                  9      Y
## 818       Married         11691       25995                  0      Y
## 258      Divorced          4779       12761                  7      Y
## 696        Single          3294       13137                  1      Y
## 812       Married          3229        4910                  4      Y
## 479       Married          2950       17363                  9      Y
## 490        Single          6142        7360                  3      Y
## 867       Married          4465       12069                  0      Y
## 471       Married          2226        6073                  1      Y
## 862        Single         10231       20364                  3      Y
## 824       Married          2610        6233                  1      Y
## 522       Married          2408        7324                  1      Y
## 401        Single          6091       24793                  2      Y
## 353       Married          4422       21203                  3      Y
## 163      Divorced          7104       20431                  0      Y
## 492       Married         16880       22422                  4      Y
## 820        Single          9854       23352                  3      Y
## 632        Single          9888        6770                  1      Y
## 384      Divorced          2322        9518                  3      Y
## 644       Married          2307       14460                  1      Y
## 792        Single          2311        5711                  2      Y
## 525       Married          2177        8456                  3      Y
## 677       Married          5228       23361                  0      Y
## 807       Married          6232       12477                  2      Y
## 825       Married          4663       12421                  9      Y
## 551       Married         19189       19562                  1      Y
## 328      Divorced          5410       11189                  6      Y
## 356        Single          4759       15891                  3      Y
## 77        Married          7083       12288                  1      Y
## 116        Single          7642        4814                  1      Y
## 450       Married          4385       24162                  1      Y
## 701       Married          2804       15434                  1      Y
## 443        Single          3485       14935                  2      Y
## 739       Married          4601        6179                  1      Y
## 835      Divorced         13577       25592                  1      Y
## 160       Married         13191       23281                  3      Y
## 850       Married          2544        7102                  0      Y
## 604       Married          3131       26342                  1      Y
## 165       Married          8020        5100                  0      Y
## 606       Married         14814       13514                  3      Y
## 524      Divorced         19502        2125                  1      Y
## 126       Married          5204       13586                  1      Y
## 93         Single          2090        2396                  6      Y
## 418       Married          3688        7122                  4      Y
## 64         Single          1514        8018                  1      Y
## 685       Married          2713        6672                  1      Y
## 293        Single          2279       11781                  1      Y
## 727       Married          2973       21222                  5      Y
## 729        Single          2340       23213                  1      Y
## 860       Married          7978       14075                  1      Y
## 609        Single          3578       23577                  0      Y
## 246      Divorced          2532        6054                  6      Y
## 375        Single          2728       21082                  1      Y
## 159       Married         10820       11535                  8      Y
## 108       Married         19636       25811                  4      Y
## 489        Single          5381       19294                  9      Y
## 391       Married          6500       13305                  5      Y
## 82        Married          6883        5151                  2      Y
## 379       Married          2168       26933                  0      Y
## 439       Married          2683        3810                  1      Y
## 681       Married          4256       18154                  1      Y
## 477      Divorced          6334       24558                  4      Y
## 314       Married          6132       13983                  2      Y
## 843       Married         13826       19028                  3      Y
## 863        Single          5304        4652                  8      Y
## 839      Divorced         10333       19271                  8      Y
## 362        Single          1091       10642                  1      Y
## 660        Single          6499       22656                  1      Y
## 643        Single         16756       17323                  7      Y
## 310       Married          4851       15678                  0      Y
## 45        Married          1223       16901                  1      Y
## 390        Single          2174        9150                  1      Y
## 193      Divorced          4810       26314                  2      Y
## 494        Single          8606       21195                  1      Y
## 34        Married          2258       16340                  6      Y
## 78         Single         17567        3156                  1      Y
## 344      Divorced          2083       22653                  1      Y
## 753       Married         11245       20689                  2      Y
## 202       Married          6220        7346                  1      Y
## 6        Divorced          8793        4809                  1      Y
## 36        Married          2794       26062                  1      Y
## 849       Married          6380        6110                  2      Y
## 340      Divorced          2341       19715                  1      Y
## 132        Single          4507        8191                  3      Y
## 373      Divorced          5210       20308                  1      Y
## 747      Divorced          2335        3157                  4      Y
## 44         Single         19613       26362                  8      Y
## 267      Divorced         17007       11929                  7      Y
## 410       Married         17159        5200                  6      Y
## 760        Single         18824        2493                  2      Y
## 788       Married          4941       17747                  2      Y
## 596      Divorced          4037       21816                  1      Y
## 302       Married          9069       11031                  1      Y
## 731      Divorced          4765       23814                  4      Y
## 56       Divorced          2289       20520                  1      Y
## 454        Single          5487       10410                  1      Y
## 382       Married          2942        8916                  1      Y
## 647        Single          1904       13556                  1      Y
## 445      Divorced         19328       14218                  7      Y
## 804        Single          9582       10333                  0      Y
## 104       Married          2819        8544                  2      Y
## 437        Single          4382       16374                  6      Y
## 656       Married          4741       22722                  1      Y
## 679      Divorced          5131        9192                  7      Y
## 752        Single          2302        8319                  1      Y
## 426      Divorced         10798        5268                  5      Y
## 296       Married          2660       20232                  7      Y
## 420       Married         11935       21526                  1      Y
## 830       Married         18200        7999                  1      Y
## 32        Married          2028       13637                  1      Y
## 726       Married          4898        7505                  0      Y
## 166        Single          2044       22052                  1      Y
## 247        Single          7143       25713                  1      Y
## 117        Single          4950       20623                  0      Y
## 845        Single          6029       25353                  5      Y
## 364      Divorced         19436        5949                  0      Y
## 557        Single          3780       23428                  7      Y
## 376        Single         14411       24450                  1      Y
## 98       Divorced          5163       15850                  5      Y
## 502        Single          2028       12947                  5      Y
## 736        Single          6500       26997                  0      Y
## 180        Single          8686       12930                  4      Y
## 338        Single          7756       22266                  0      Y
## 501       Married         10008       12023                  7      Y
## 778      Divorced          2827       14947                  1      Y
## 531       Married          8823       24608                  0      Y
## 744        Single          3295       12862                  1      Y
## 196       Married          2723       23231                  1      Y
## 30        Married          4258       26589                  0      Y
## 31       Divorced          4668       22812                  0      Y
## 400        Single         10209       19719                  5      Y
## 95         Single         19926       17053                  3      Y
## 49         Single          1878        8059                  1      Y
## 771        Single          4682        4317                  3      Y
## 659        Single          6032       10110                  6      Y
## 465       Married          2437       15587                  9      Y
## 698        Single          5914        9945                  8      Y
## 798       Married          2398       15999                  1      Y
## 782        Single          4553       20978                  1      Y
## 291      Divorced          2270       11005                  3      Y
## 764       Married          6347       24920                  0      Y
## 211       Married         13570        5640                  0      Y
## 634      Divorced          2835        2561                  5      Y
## 654       Married         12504       23978                  3      Y
## 775       Married         10124       18611                  2      Y
## 559       Married          4434       11806                  1      Y
## 805       Married          2042       25043                  4      Y
## 178       Married          2042       15346                  6      Y
## 110        Single          2439       11288                  1      Y
## 857        Single          2657        8556                  5      Y
## 337      Divorced          4968       18500                  1      Y
## 115       Married          8966       21026                  3      Y
## 585       Married          6465       19121                  2      Y
## 460        Single          5810       22604                  1      Y
## 505       Married         17169       26703                  3      Y
## 576      Divorced          5993        2689                  1      Y
## 799        Single          1859        6148                  1      Y
## 856       Married          2404       16192                  1      Y
## 233       Married         12936       24164                  7      Y
## 780       Married          4787       26124                  9      Y
## 129        Single          4081       20003                  1      Y
## 837       Married          2996        5182                  7      Y
## 766        Single          5769       23447                  1      Y
## 569        Single          6244        7824                  7      Y
## 388      Divorced          4262       22645                  4      Y
## 321       Married          2332        3974                  6      Y
## 308       Married          2979       22478                  3      Y
## 829       Married          2468       15963                  4      Y
## 94         Single          6142        4223                  3      Y
## 289       Married          9738       22952                  0      Y
## 408      Divorced          2741        6865                  1      Y
## 368       Married          5473       19345                  0      Y
## 164        Single          6545       23016                  3      Y
## 413       Married          3022       10227                  4      Y
## 487        Single          1081       16019                  1      Y
## 767       Married          3376       18863                  1      Y
## 733       Married          2863       19555                  1      Y
## 735        Single          2972       22061                  1      Y
## 808       Married          2323       11992                  1      Y
## 458        Single          5441        8423                  0      Y
## 552      Divorced          4936       23965                  1      Y
## 53        Married          2011       19982                  1      Y
## 409       Married         15427       22021                  2      Y
## 702       Married          5714        5829                  1      Y
## 280       Married          4035       16143                  0      Y
## 327      Divorced         19049        3549                  0      Y
## 415      Divorced          2351       12253                  0      Y
## 83        Married          5321       14284                  2      Y
## 396      Divorced          2695        7747                  0      Y
## 133       Married          4523        4386                  0      Y
## 275        Single         18722       13339                  8      Y
## 456        Single         19833        4349                  1      Y
## 518      Divorced          2766        8952                  8      Y
## 304        Single          5098       18698                  1      Y
## 238       Married          6322       18089                  1      Y
## 507        Single          2851        9150                  1      Y
## 482       Married          2356       14871                  3      Y
## 476        Single          5772       20445                  4      Y
## 434      Divorced          2109       10007                  1      Y
## 495       Married          5673        6060                  1      Y
## 724        Single          8858       15669                  0      Y
## 47         Single          6735       12147                  6      Y
## 147       Married          2756        4673                  1      Y
## 331        Single         15379       22384                  4      Y
## 218      Divorced         17174        2437                  3      Y
## 555       Married          4876       14242                  9      Y
## 201        Single         13194       17071                  4      Y
## 587      Divorced          5329       15717                  7      Y
## 290       Married          2572       20317                  1      Y
## 467        Single          2644       17001                  3      Y
## 393      Divorced          3065        3995                  1      Y
## 255      Divorced         13757       25178                  2      Y
## 558       Married          3622       22794                  1      Y
## 300       Married          3464       24737                  5      Y
## 253        Single          2274        6153                  1      Y
## 592        Single          4599        7815                  0      Y
## 59       Divorced         13603       11677                  2      Y
## 811      Divorced          3072       11012                  1      Y
## 278        Single          2455       10675                  0      Y
## 137       Married          2319        6689                  1      Y
## 225      Divorced          4771       14293                  2      Y
## 191       Married          5775       11934                  1      Y
## 544       Married          6553        7259                  9      Y
## 473       Married         13245       15067                  4      Y
## 523      Divorced          9724       18787                  2      Y
## 710      Divorced         17924        4544                  1      Y
## 861        Single          2838        4257                  0      Y
## 182      Divorced          6893       19461                  3      Y
## 852        Single          2206       16117                  1      Y
## 236       Married         10609       14922                  5      Y
## 779        Single          2570       11925                  1      Y
## 187      Divorced          5206        4973                  1      Y
## 717       Married          6578        2706                  1      Y
## 740        Single          3681       14004                  4      Y
## 629       Married         10976       15813                  3      Y
## 311       Married          5484       13008                  9      Y
## 399      Divorced          6306       26236                  1      Y
## 131      Divorced          4187        3356                  1      Y
## 486        Single          5993       19479                  8      Y
## 370        Single          5055       10557                  7      Y
## 199       Married         10248        2094                  3      Y
## 783      Divorced          2277       22650                  3      Y
## 566        Single          2436       13422                  6      Y
## 226       Married          3748        4077                  1      Y
## 488      Divorced          2911       15170                  1      Y
## 614        Single          2121        9947                  1      Y
## 320        Single          2994       21221                  1      Y
## 800      Divorced          6842       26308                  6      Y
## 700       Married         19081       10849                  5      Y
## 762       Married         16752       12982                  1      Y
## 684      Divorced          9439       23402                  3      Y
## 220        Single          2875        9973                  1      Y
## 229       Married          4999       17519                  0      Y
## 11        Married         19392       22539                  7      Y
## 342       Married          3867       14222                  1      Y
## 716       Married          1555       11585                  1      Y
## 65        Married          2210        3372                  1      Y
## 259       Married          9713       24444                  2      Y
## 540        Single          5155        2253                  7      Y
## 24         Single          5332       21602                  7      Y
## 134       Married          5295        7693                  4      Y
## 590       Married          4014       16002                  3      Y
## 286       Married          5207       22949                  1      Y
## 12       Divorced          2706       10494                  1      Y
## 249      Divorced          5467       13953                  3      Y
## 529        Single          2859       26278                  1      Y
## 215        Single          2045       15174                  0      Y
## 449        Single          2406        5456                  1      Y
## 20        Married          5033        9364                  2      Y
## 530       Married          4963        4510                  9      Y
## 294       Married         11957       17231                  0      Y
## 89         Single          8726        2975                  1      Y
## 435      Divorced          9705       20652                  2      Y
## 436       Married          6214        3415                  1      Y
## 330       Married         16799       16616                  0      Y
## 639       Married          2725       21630                  1      Y
## 663      Divorced         10368        5596                  4      Y
## 469        Single          4969       21813                  8      Y
## 868       Married          4591       24200                  3      Y
## 713        Single          2559       16620                  5      Y
## 594       Married          3211       22102                  1      Y
## 579        Single          5042        3140                  0      Y
## 646       Married         10855        8552                  7      Y
## 633        Single          6861        4981                  8      Y
## 581       Married          3669        9075                  3      Y
## 513       Married          3433       17360                  6      Y
## 619       Married          9991       21457                  4      Y
## 282        Single          4721        3119                  2      Y
## 791       Married          4789       23070                  4      Y
## 699        Single          1569       18420                  1      Y
## 601       Married          2033        7103                  1      Y
## 210      Divorced          8189       21196                  3      Y
## 172       Married          5467        2125                  8      Y
## 22        Married          8120       18597                  3      Y
## 453        Single          5813       13492                  1      Y
## 776        Single          3505       19630                  1      Y
## 626      Divorced          2517        3208                  1      Y
## 252      Divorced          5079       25952                  4      Y
## 480       Married         13744       15471                  1      Y
## 648       Married          2305        6217                  1      Y
## 840       Married         19665       13583                  4      Y
## 814       Married         19658        5220                  3      Y
## 285       Married          2844        6004                  1      Y
## 438        Single          2867       20006                  0      Y
## 279        Single          5744       26959                  1      Y
## 203       Married          6162       19124                  1      Y
## 405        Single          2559       17852                  1      Y
## 138       Married         10739       13943                  8      Y
## 217       Married          9824       22174                  3      Y
## 854        Single          5661        4824                  0      Y
##     OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 834       No                25                 4                        3
## 697       No                25                 4                        2
## 207       No                13                 3                        2
## 715       No                15                 3                        2
## 809       No                12                 3                        2
## 725       No                13                 3                        4
## 222       No                14                 3                        4
## 71        No                22                 4                        1
## 503      Yes                11                 3                        3
## 403       No                12                 3                        3
## 387       No                14                 3                        1
## 806      Yes                13                 3                        4
## 826       No                17                 3                        4
## 821       No                17                 3                        4
## 844       No                11                 3                        4
## 528      Yes                12                 3                        4
## 128       No                13                 3                        4
## 539      Yes                16                 3                        2
## 457       No                11                 3                        1
## 732      Yes                18                 3                        1
## 842       No                13                 3                        1
## 85       Yes                11                 3                        4
## 122      Yes                11                 3                        4
## 474       No                13                 3                        2
## 819       No                13                 3                        4
## 625       No                11                 3                        3
## 184       No                18                 3                        4
## 92        No                13                 3                        1
## 269       No                20                 4                        3
## 463       No                11                 3                        4
## 407       No                14                 3                        1
## 235      Yes                12                 3                        4
## 54        No                12                 3                        2
## 146       No                14                 3                        1
## 464       No                20                 4                        4
## 192       No                14                 3                        4
## 665       No                20                 4                        3
## 394       No                19                 3                        4
## 810      Yes                16                 3                        4
## 512       No                21                 4                        2
## 628      Yes                14                 3                        2
## 248       No                14                 3                        2
## 8        Yes                14                 3                        3
## 468       No                12                 3                        2
## 851      Yes                11                 3                        3
## 404       No                12                 3                        3
## 365       No                14                 3                        4
## 491      Yes                11                 3                        4
## 616       No                14                 3                        3
## 748       No                20                 4                        3
## 123      Yes                12                 3                        3
## 841       No                13                 3                        3
## 326       No                19                 3                        3
## 149       No                22                 4                        4
## 91       Yes                23                 4                        3
## 72        No                18                 3                        4
## 563       No                14                 3                        2
## 80       Yes                11                 3                        3
## 828      Yes                12                 3                        4
## 481       No                12                 3                        1
## 466      Yes                11                 3                        3
## 510       No                12                 3                        4
## 381       No                11                 3                        2
## 90        No                12                 3                        4
## 127      Yes                22                 4                        2
## 737       No                11                 3                        1
## 527      Yes                11                 3                        1
## 100       No                17                 3                        1
## 209       No                14                 3                        4
## 148       No                13                 3                        4
## 125       No                18                 3                        1
## 754       No                21                 4                        3
## 139      Yes                21                 4                        4
## 649       No                14                 3                        2
## 516       No                18                 3                        4
## 3         No                11                 3                        3
## 412       No                12                 3                        2
## 667       No                14                 3                        4
## 227       No                12                 3                        3
## 4         No                19                 3                        3
## 27        No                11                 3                        4
## 240       No                15                 3                        3
## 169      Yes                12                 3                        2
## 422      Yes                15                 3                        1
## 130       No                12                 3                        4
## 784       No                12                 3                        1
## 188       No                14                 3                        3
## 521       No                16                 3                        3
## 745      Yes                22                 4                        2
## 256       No                13                 3                        3
## 145       No                20                 4                        2
## 262       No                13                 3                        3
## 369       No                16                 3                        4
## 573      Yes                12                 3                        3
## 135       No                11                 3                        2
## 152       No                12                 3                        4
## 374       No                11                 3                        1
## 822       No                14                 3                        4
## 785       No                11                 3                        4
## 446       No                12                 3                        3
## 793      Yes                19                 3                        4
## 17        No                12                 3                        3
## 718       No                13                 3                        4
## 176      Yes                11                 3                        3
## 319      Yes                12                 3                        4
## 372       No                11                 3                        3
## 461       No                17                 3                        4
## 361       No                14                 3                        1
## 141      Yes                12                 3                        1
## 28       Yes                17                 3                        1
## 423      Yes                18                 3                        2
## 363       No                15                 3                        4
## 796      Yes                11                 3                        1
## 859       No                21                 4                        4
## 295      Yes                21                 4                        4
## 574       No                21                 4                        3
## 703      Yes                11                 3                        2
## 334       No                11                 3                        4
## 268       No                12                 3                        1
## 272       No                12                 3                        3
## 671       No                13                 3                        1
## 51        No                17                 3                        4
## 153       No                14                 3                        3
## 441      Yes                14                 3                        4
## 154       No                12                 3                        1
## 87        No                12                 3                        4
## 575       No                20                 4                        4
## 414       No                13                 3                        3
## 763       No                13                 3                        1
## 58       Yes                18                 3                        4
## 224       No                14                 3                        4
## 580      Yes                11                 3                        2
## 622      Yes                19                 3                        3
## 155       No                14                 3                        2
## 847       No                14                 3                        4
## 542       No                19                 3                        2
## 395      Yes                14                 3                        4
## 67        No                13                 3                        2
## 25        No                14                 3                        1
## 288       No                11                 3                        3
## 358       No                16                 3                        2
## 827       No                13                 3                        1
## 728      Yes                17                 3                        4
## 730       No                14                 3                        3
## 303       No                12                 3                        4
## 69        No                13                 3                        4
## 264       No                15                 3                        2
## 562      Yes                14                 3                        2
## 675       No                17                 3                        1
## 427       No                13                 3                        2
## 223      Yes                25                 4                        3
## 35        No                24                 4                        2
## 533       No                24                 4                        3
## 239       No                14                 3                        3
## 243       No                23                 4                        4
## 707       No                15                 3                        2
## 43       Yes                16                 3                        2
## 777       No                21                 4                        1
## 419       No                13                 3                        2
## 333      Yes                12                 3                        3
## 640       No                11                 3                        2
## 757      Yes                25                 4                        4
## 774      Yes                17                 3                        1
## 864      Yes                12                 3                        4
## 499       No                13                 3                        2
## 484      Yes                18                 3                        3
## 281      Yes                17                 3                        1
## 84       Yes                13                 3                        1
## 761      Yes                20                 4                        3
## 37        No                17                 3                        3
## 292       No                15                 3                        3
## 537       No                16                 3                        3
## 118      Yes                13                 3                        1
## 261       No                14                 3                        3
## 213       No                17                 3                        1
## 459       No                22                 4                        4
## 593       No                15                 3                        4
## 448       No                18                 3                        3
## 26        No                11                 3                        3
## 599      Yes                20                 4                        2
## 113       No                19                 3                        4
## 345       No                19                 3                        3
## 870       No                11                 3                        4
## 177      Yes                13                 3                        4
## 156      Yes                12                 3                        2
## 568       No                11                 3                        4
## 673       No                14                 3                        4
## 432       No                16                 3                        2
## 759       No                19                 3                        4
## 440       No                13                 3                        3
## 347       No                11                 3                        4
## 833       No                12                 3                        3
## 274       No                13                 3                        1
## 40        No                12                 3                        1
## 636       No                13                 3                        2
## 2         No                14                 3                        1
## 216       No                14                 3                        3
## 312       No                15                 3                        3
## 305       No                24                 4                        1
## 838       No                12                 3                        4
## 686       No                25                 4                        4
## 162       No                12                 3                        1
## 866       No                11                 3                        3
## 832      Yes                19                 3                        4
## 627      Yes                12                 3                        3
## 329      Yes                16                 3                        3
## 179       No                14                 3                        4
## 545       No                11                 3                        3
## 397       No                13                 3                        3
## 185       No                11                 3                        1
## 357       No                19                 3                        3
## 170       No                22                 4                        1
## 251       No                12                 3                        3
## 638       No                18                 3                        3
## 869      Yes                19                 3                        3
## 429      Yes                14                 3                        4
## 57       Yes                12                 3                        4
## 705       No                17                 3                        4
## 55        No                23                 4                        4
## 853      Yes                12                 3                        4
## 478       No                12                 3                        1
## 797      Yes                14                 3                        2
## 770       No                13                 3                        4
## 257       No                11                 3                        3
## 79        No                12                 3                        4
## 66       Yes                12                 3                        4
## 687      Yes                12                 3                        1
## 339      Yes                14                 3                        4
## 631       No                20                 4                        2
## 676       No                14                 3                        3
## 143       No                13                 3                        2
## 561       No                12                 3                        3
## 254       No                17                 3                        3
## 189       No                14                 3                        1
## 1         No                11                 3                        3
## 858       No                24                 4                        1
## 578       No                14                 3                        4
## 234       No                14                 3                        4
## 751      Yes                14                 3                        2
## 299      Yes                22                 4                        1
## 543      Yes                15                 3                        4
## 214      Yes                13                 3                        4
## 366       No                11                 3                        4
## 624      Yes                16                 3                        3
## 610       No                19                 3                        4
## 250       No                16                 3                        2
## 119       No                12                 3                        1
## 103       No                15                 3                        1
## 221       No                22                 4                        3
## 630       No                14                 3                        2
## 605       No                13                 3                        3
## 462       No                11                 3                        3
## 714       No                11                 3                        1
## 517      Yes                18                 3                        3
## 287      Yes                23                 4                        1
## 519       No                12                 3                        1
## 674      Yes                19                 3                        1
## 392      Yes                19                 3                        3
## 758       No                14                 3                        2
## 514      Yes                12                 3                        3
## 668       No                13                 3                        1
## 317       No                18                 3                        4
## 52       Yes                21                 4                        3
## 511       No                11                 3                        3
## 815       No                19                 3                        4
## 341      Yes                14                 3                        4
## 612      Yes                17                 3                        1
## 564       No                20                 4                        3
## 183       No                12                 3                        4
## 97        No                12                 3                        4
## 602       No                14                 3                        2
## 711      Yes                23                 4                        4
## 586      Yes                15                 3                        3
## 642       No                12                 3                        2
## 651       No                19                 3                        2
## 666      Yes                12                 3                        4
## 508       No                11                 3                        2
## 70        No                12                 3                        2
## 73       Yes                13                 3                        1
## 509       No                17                 3                        3
## 704      Yes                11                 3                        3
## 455       No                11                 3                        1
## 662       No                12                 3                        3
## 7        Yes                12                 3                        1
## 198       No                17                 3                        2
## 442       No                17                 3                        3
## 756       No                14                 3                        3
## 322       No                12                 3                        4
## 265       No                12                 3                        3
## 150       No                21                 4                        2
## 232       No                13                 3                        1
## 421       No                15                 3                        3
## 231       No                19                 3                        1
## 786       No                11                 3                        3
## 241      Yes                21                 4                        4
## 228      Yes                16                 3                        1
## 284       No                12                 3                        4
## 99        No                13                 3                        3
## 181       No                18                 3                        2
## 496       No                14                 3                        3
## 39        No                14                 3                        1
## 212       No                13                 3                        2
## 107       No                13                 3                        1
## 325       No                16                 3                        3
## 635       No                16                 3                        1
## 343       No                15                 3                        3
## 237       No                11                 3                        1
## 206      Yes                13                 3                        4
## 607       No                11                 3                        4
## 318       No                12                 3                        2
## 772       No                25                 4                        3
## 645       No                17                 3                        1
## 749       No                13                 3                        2
## 385      Yes                12                 3                        3
## 794       No                13                 3                        2
## 313       No                12                 3                        1
## 380      Yes                11                 3                        2
## 260      Yes                19                 3                        3
## 10        No                14                 3                        2
## 447       No                15                 3                        4
## 534       No                18                 3                        2
## 497       No                21                 4                        4
## 352       No                11                 3                        2
## 746       No                14                 3                        2
## 158       No                11                 3                        4
## 483      Yes                15                 3                        2
## 323       No                12                 3                        2
## 324      Yes                23                 4                        1
## 535      Yes                11                 3                        4
## 307       No                18                 3                        2
## 694      Yes                23                 4                        3
## 750       No                14                 3                        1
## 16        No                13                 3                        4
## 855      Yes                11                 3                        4
## 823       No                21                 4                        4
## 591      Yes                14                 3                        2
## 360       No                11                 3                        3
## 470       No                11                 3                        3
## 68        No                13                 3                        3
## 548       No                14                 3                        4
## 670       No                14                 3                        1
## 424       No                15                 3                        3
## 121       No                14                 3                        1
## 818       No                11                 3                        4
## 258       No                14                 3                        2
## 696      Yes                18                 3                        1
## 812       No                11                 3                        2
## 479       No                13                 3                        3
## 490       No                11                 3                        4
## 867       No                18                 3                        1
## 471       No                11                 3                        3
## 862       No                14                 3                        4
## 824       No                12                 3                        3
## 522      Yes                17                 3                        3
## 401       No                20                 4                        3
## 353      Yes                13                 3                        4
## 163       No                12                 3                        4
## 492      Yes                11                 3                        2
## 820      Yes                11                 3                        4
## 632       No                21                 4                        1
## 384       No                13                 3                        3
## 644      Yes                23                 4                        2
## 792       No                15                 3                        4
## 525       No                17                 3                        1
## 677       No                15                 3                        1
## 807       No                11                 3                        2
## 825      Yes                12                 3                        2
## 551       No                12                 3                        2
## 328      Yes                17                 3                        3
## 356       No                18                 3                        4
## 77       Yes                14                 3                        4
## 116      Yes                13                 3                        4
## 450       No                15                 3                        1
## 701       No                11                 3                        4
## 443       No                11                 3                        3
## 739       No                16                 3                        2
## 835      Yes                15                 3                        4
## 160      Yes                17                 3                        3
## 850       No                18                 3                        1
## 604       No                13                 3                        1
## 165       No                15                 3                        3
## 606       No                19                 3                        3
## 524      Yes                17                 3                        3
## 126      Yes                11                 3                        4
## 93       Yes                15                 3                        2
## 418       No                18                 3                        4
## 64        No                16                 3                        3
## 685       No                11                 3                        3
## 293       No                16                 3                        4
## 727       No                15                 3                        2
## 729      Yes                18                 3                        2
## 860       No                11                 3                        4
## 609       No                12                 3                        4
## 246       No                14                 3                        3
## 375       No                11                 3                        1
## 159       No                11                 3                        3
## 108      Yes                18                 3                        1
## 489      Yes                13                 3                        4
## 391       No                17                 3                        2
## 82        No                16                 3                        2
## 379      Yes                18                 3                        1
## 439      Yes                14                 3                        3
## 681       No                12                 3                        1
## 477       No                19                 3                        4
## 314       No                17                 3                        3
## 843       No                22                 4                        3
## 863      Yes                13                 3                        2
## 839      Yes                12                 3                        3
## 362       No                17                 3                        4
## 660       No                13                 3                        3
## 643       No                15                 3                        2
## 310       No                22                 4                        3
## 45        No                22                 4                        4
## 390      Yes                11                 3                        3
## 193       No                14                 3                        3
## 494       No                19                 3                        4
## 34        No                12                 3                        2
## 78        No                15                 3                        2
## 344       No                20                 4                        3
## 753      Yes                15                 3                        3
## 202       No                17                 3                        2
## 6         No                21                 4                        3
## 36        No                20                 4                        3
## 849      Yes                12                 3                        1
## 340       No                13                 3                        4
## 132       No                12                 3                        3
## 373       No                18                 3                        1
## 747      Yes                15                 3                        4
## 44        No                22                 4                        4
## 267       No                14                 3                        4
## 410       No                24                 4                        3
## 760      Yes                16                 3                        1
## 788       No                15                 3                        1
## 596       No                22                 4                        1
## 302       No                22                 4                        4
## 731       No                21                 4                        3
## 56        No                20                 4                        2
## 454       No                14                 3                        2
## 382       No                23                 4                        4
## 647       No                12                 3                        4
## 445      Yes                17                 3                        3
## 804      Yes                22                 4                        1
## 104       No                16                 3                        1
## 437       No                17                 3                        4
## 656      Yes                13                 3                        3
## 679       No                13                 3                        2
## 752      Yes                11                 3                        1
## 426       No                13                 3                        3
## 296      Yes                11                 3                        3
## 420       No                18                 3                        3
## 830       No                11                 3                        3
## 32        No                18                 3                        4
## 726       No                12                 3                        4
## 166       No                13                 3                        4
## 247      Yes                14                 3                        3
## 117       No                14                 3                        2
## 845       No                12                 3                        1
## 364       No                19                 3                        4
## 557       No                11                 3                        3
## 376      Yes                13                 3                        4
## 98        No                14                 3                        4
## 502      Yes                14                 3                        2
## 736       No                14                 3                        2
## 180       No                22                 4                        3
## 338       No                17                 3                        3
## 501      Yes                14                 3                        4
## 778       No                12                 3                        3
## 531       No                18                 3                        1
## 744       No                13                 3                        3
## 196       No                11                 3                        2
## 30        No                18                 3                        1
## 31        No                17                 3                        4
## 400      Yes                18                 3                        2
## 95        No                15                 3                        2
## 49       Yes                14                 3                        4
## 771       No                14                 3                        3
## 659      Yes                15                 3                        4
## 465      Yes                16                 3                        4
## 698       No                16                 3                        4
## 798      Yes                17                 3                        3
## 782       No                11                 3                        1
## 291       No                14                 3                        4
## 764       No                12                 3                        1
## 211       No                23                 4                        3
## 634       No                22                 4                        1
## 654       No                21                 4                        2
## 775      Yes                14                 3                        3
## 559       No                13                 3                        4
## 805       No                12                 3                        3
## 178      Yes                14                 3                        2
## 110       No                14                 3                        4
## 857      Yes                11                 3                        3
## 337       No                11                 3                        4
## 115      Yes                15                 3                        4
## 585      Yes                13                 3                        4
## 460       No                16                 3                        3
## 505       No                19                 3                        2
## 576       No                18                 3                        3
## 799      Yes                25                 4                        2
## 856       No                13                 3                        1
## 233       No                11                 3                        3
## 780      Yes                14                 3                        2
## 129      Yes                14                 3                        1
## 837      Yes                15                 3                        4
## 766      Yes                14                 3                        1
## 569       No                17                 3                        1
## 388       No                12                 3                        2
## 321       No                20                 4                        3
## 308       No                17                 3                        4
## 829       No                14                 3                        2
## 94       Yes                16                 3                        3
## 289       No                14                 3                        3
## 408       No                14                 3                        3
## 368       No                12                 3                        4
## 164      Yes                13                 3                        3
## 413       No                21                 4                        1
## 487       No                13                 3                        3
## 767       No                13                 3                        3
## 733       No                12                 3                        1
## 735       No                13                 3                        3
## 808       No                24                 4                        1
## 458      Yes                22                 4                        4
## 552       No                13                 3                        4
## 53        No                13                 3                        4
## 409       No                16                 3                        3
## 702       No                20                 4                        1
## 280      Yes                16                 3                        2
## 327      Yes                14                 3                        4
## 415       No                16                 3                        4
## 83        No                11                 3                        4
## 396      Yes                18                 3                        2
## 133       No                11                 3                        4
## 275       No                11                 3                        4
## 456       No                14                 3                        2
## 518       No                22                 4                        2
## 304       No                19                 3                        2
## 238      Yes                12                 3                        4
## 507      Yes                13                 3                        2
## 482      Yes                19                 3                        2
## 476      Yes                21                 4                        3
## 434       No                13                 3                        3
## 495      Yes                13                 3                        1
## 724       No                11                 3                        2
## 47        No                15                 3                        2
## 147       No                13                 3                        4
## 331       No                14                 3                        1
## 218       No                11                 3                        2
## 555       No                14                 3                        4
## 201      Yes                16                 3                        4
## 587      Yes                12                 3                        4
## 290       No                16                 3                        2
## 467      Yes                21                 4                        4
## 393      Yes                13                 3                        4
## 255       No                11                 3                        3
## 558      Yes                13                 3                        4
## 300      Yes                13                 3                        4
## 253       No                14                 3                        4
## 592      Yes                23                 4                        3
## 59       Yes                18                 3                        1
## 811       No                11                 3                        3
## 278       No                19                 3                        1
## 137      Yes                11                 3                        4
## 225       No                19                 3                        4
## 191       No                13                 3                        4
## 544       No                14                 3                        2
## 473      Yes                14                 3                        2
## 523       No                17                 3                        3
## 710       No                11                 3                        4
## 861       No                14                 3                        2
## 182       No                15                 3                        4
## 852       No                13                 3                        1
## 236       No                11                 3                        3
## 779       No                20                 4                        3
## 187       No                17                 3                        3
## 717       No                18                 3                        1
## 740       No                14                 3                        4
## 629       No                18                 3                        2
## 311       No                17                 3                        2
## 399       No                21                 4                        1
## 131      Yes                13                 3                        2
## 486      Yes                11                 3                        1
## 370       No                16                 3                        3
## 199       No                14                 3                        4
## 783      Yes                11                 3                        3
## 566      Yes                12                 3                        3
## 226       No                13                 3                        3
## 488       No                17                 3                        4
## 614      Yes                13                 3                        2
## 320      Yes                12                 3                        4
## 800       No                20                 4                        1
## 700       No                11                 3                        1
## 762      Yes                11                 3                        3
## 684      Yes                16                 3                        2
## 220      Yes                20                 4                        2
## 229       No                21                 4                        1
## 11        No                13                 3                        4
## 342      Yes                12                 3                        2
## 716       No                11                 3                        3
## 65        No                13                 3                        1
## 259      Yes                13                 3                        4
## 540       No                13                 3                        4
## 24        No                13                 3                        4
## 134       No                21                 4                        3
## 590      Yes                15                 3                        3
## 286      Yes                12                 3                        2
## 12        No                15                 3                        2
## 249      Yes                14                 3                        1
## 529       No                18                 3                        1
## 215       No                13                 3                        4
## 449       No                11                 3                        4
## 20        No                15                 3                        4
## 530      Yes                18                 3                        1
## 294       No                18                 3                        1
## 89        No                15                 3                        4
## 435       No                12                 3                        2
## 436       No                18                 3                        1
## 330       No                14                 3                        3
## 639      Yes                11                 3                        2
## 663      Yes                12                 3                        2
## 469       No                18                 3                        4
## 868      Yes                17                 3                        3
## 713       No                11                 3                        3
## 594       No                14                 3                        4
## 579       No                13                 3                        4
## 646       No                11                 3                        1
## 633      Yes                12                 3                        3
## 581       No                11                 3                        3
## 513       No                13                 3                        1
## 619       No                15                 3                        1
## 282      Yes                13                 3                        3
## 791       No                25                 4                        1
## 699      Yes                12                 3                        3
## 601       No                13                 3                        3
## 210      Yes                13                 3                        3
## 172       No                18                 3                        3
## 22        No                12                 3                        4
## 453      Yes                18                 3                        4
## 776       No                18                 3                        4
## 626       No                11                 3                        2
## 252       No                13                 3                        4
## 480      Yes                25                 4                        1
## 648       No                15                 3                        3
## 840       No                12                 3                        4
## 814       No                11                 3                        3
## 285       No                13                 3                        4
## 438       No                13                 3                        4
## 279      Yes                11                 3                        4
## 203       No                12                 3                        3
## 405       No                11                 3                        4
## 138       No                11                 3                        3
## 217       No                19                 3                        3
## 854       No                19                 3                        3
##     StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
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##     YearsSinceLastPromotion YearsWithCurrManager
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ddsTest = DDSAnalytics[-ddsObs,]

DDSAnalytics$YearsAtCompany_log = log(DDSAnalytics$YearsAtCompany)
DDSAnalytics$MonthlyRate_log = log(DDSAnalytics$MonthlyRate)
DDSAnalytics$NumCompaniesWorked_log = log(DDSAnalytics$NumCompaniesWorked) # many -inf no use 

# fitting or training our model on Training Set 
ddsModel <- lm(MonthlyIncome ~ Age + NumCompaniesWorked + JobSatisfaction+JobLevel+
    YearsAtCompany +TotalWorkingYears, data = ddsTrain) # train data model 

summary(ddsModel)
## 
## Call:
## lm(formula = MonthlyIncome ~ Age + NumCompaniesWorked + JobSatisfaction + 
##     JobLevel + YearsAtCompany + TotalWorkingYears, data = ddsTrain)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5709.8  -841.0    58.5   719.4  3827.9 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1624.093    291.344  -5.574 3.65e-08 ***
## Age                   -3.971      8.175  -0.486  0.62728    
## NumCompaniesWorked   -18.279     23.638  -0.773  0.43964    
## JobSatisfaction        6.326     47.176   0.134  0.89337    
## JobLevel            3665.519     77.093  47.547  < 2e-16 ***
## YearsAtCompany       -40.092     13.134  -3.053  0.00236 ** 
## TotalWorkingYears     91.067     16.487   5.524 4.82e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1340 on 645 degrees of freedom
## Multiple R-squared:  0.9191, Adjusted R-squared:  0.9184 
## F-statistic:  1221 on 6 and 645 DF,  p-value: < 2.2e-16
dfres <- data.frame(ddsModel$residuals)
dfres%>%ggplot(aes(ddsModel.residuals,fill="#DC143C"))+geom_histogram(color="white")+ggtitle("Residual Histogram")+xlab("Residuals")+
  theme(panel.background = element_blank(),
        legend.position =  "none",
        plot.title = element_text(hjust = 0.5))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#hist(ddsModel$residuals,col="#1E90FF",main = "Residual Histogram")
plot(ddsModel$residuals,col="#F8766D",main = "Residual Plots")

library(ggpubr)
ggqqplot(ddsModel$residuals)

# #1E90FF", "#DC143C
# Testing our model built using train set with the test data set 
ddsTest$MonthlyIncomePred = predict(ddsModel, newdata = ddsTest)

# MS_PE = data.frame(Observed = ddsTest$MonthlyIncome, Predicted = ddsTest$MonthlyIncomePred)
# # calculating MSPE to see how good our model is.
# MS_PE$Residual = MS_PE$Observed - MS_PE$Predicted
# MS_PE$SquaredResidual = MS_PE$Residual^2
# 
# mean(MS_PE$SquaredResidual)
# RMSE(MS_PE$Observed,MS_PE$Predicted)

Monthly Income Predicton

# I have also provided an additional data set of 300 observations that do not have the Monthly Incomes.  This data is in the file “CaseStudy2CompSet No Salary.csv”. 

# Importing No Salary data to predict the salary 
DDSAnalyticsNoSalary <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Salary.csv",sep = ",",header = TRUE)

# Use model to predict the unkown Montly Income from dataset. 
ddsNoSalPreds = predict(ddsModel,newdata = DDSAnalyticsNoSalary)
as.data.frame(ddsNoSalPreds)->ddsAnalyticSalPreds 
names(ddsAnalyticSalPreds) <- c("Predicted Salary")
head(ddsAnalyticSalPreds)
##   Predicted Salary
## 1         5900.026
## 2         2215.897
## 3        14152.723
## 4         2147.704
## 5         2279.071
## 6         7212.705
DDSAnalyticsNoSalary$PredictedMonthlyIncome = ddsNoSalPreds #add PredictedSalary col and put the predicted values into tht column.
library(dplyr)
DDSAnalyticsNoSalary%>%dplyr::select(ID,PredictedMonthlyIncome)->PredictionWithID
head(PredictionWithID) # dataframe with ID and PredictedMonthlyIncome. 
##    ID PredictedMonthlyIncome
## 1 871               5900.026
## 2 872               2215.897
## 3 873              14152.723
## 4 874               2147.704
## 5 875               2279.071
## 6 876               7212.705
write.csv(PredictionWithID,"DDSIncomePrediction.csv") # write csv file to Excel

Turnover Analysis

#Turnover Analysis
#https://corpgov.law.harvard.edu/2018/01/22/the-option-to-quit-the-effect-of-employee-stock-options-on-turnover/
library(plyr)
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
## 
## Attaching package: 'plyr'
## The following object is masked from 'package:ggpubr':
## 
##     mutate
## The following object is masked from 'package:maps':
## 
##     ozone
## The following object is masked from 'package:purrr':
## 
##     compact
## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
library(dplyr)
library(tidyverse)

# Donut chart Attrition Percentage 
Att <- DDSAnalytics%>%group_by(Attrition)%>%
  dplyr::summarize(count=n(),
                   percentage = n()/nrow(DDSAnalytics))
Att
## # A tibble: 2 x 3
##   Attrition count percentage
##   <fct>     <int>      <dbl>
## 1 No          730      0.839
## 2 Yes         140      0.161
Attdonut <- ggplot(data = Att,aes(x=2,y=percentage,fill=Attrition))+
  geom_col(color="white")+
  coord_polar("y", start = 1)+
  geom_text(aes(label=paste0(round(percentage*100),"%")),
            position = position_stack(vjust = 0.5))+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5,size = I(17)))+
  ggtitle("Attrition Percentage")+
  xlim(0.5,2.5)
Attdonut

# Donut chart Marital Sttus percentage 
ddsMaritalStat <- DDSAnalytics%>%group_by(MaritalStatus)%>%
  dplyr::summarize(count=n(),
                   Mpercentage=n()/nrow(DDSAnalytics))
ddsMaritalStatDonut <- ggplot(data = ddsMaritalStat,aes(x=2,y=Mpercentage,fill=MaritalStatus))+
  geom_col(color="white")+
  coord_polar("y",start=1)+
  geom_text(aes(label=paste0(round(Mpercentage*100),"%")),
            position = position_stack(vjust = 0.5),color="white",size=I(4.5))+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        plot.title = element_text(hjust = 0.5,size = I(17)))+
  ggtitle("Marital Status Percentage")+
  xlim(0.3,2.5)
ddsMaritalStatDonut

DDSAnalytics$StockOptionLevel = as.factor(DDSAnalytics$StockOptionLevel)
DDSAnalytics$JobInvolvement = as.factor(DDSAnalytics$JobInvolvement)
#JobInvolvement Donut chart percentage 
ddsJobInv <- DDSAnalytics%>%group_by(JobInvolvement)%>%
  dplyr::summarize(count=n(),
                   InvPercentage = n()/nrow(DDSAnalytics))

ddsJobInvDonut <- ggplot(data = ddsJobInv, aes(x=2,y=InvPercentage,fill=JobInvolvement))+
  geom_col(color="white")+
  coord_polar("y",start = 1)+
  geom_text(aes(label=paste0(round(InvPercentage*100),"%")),
                  position=position_stack(vjust = 0.5),size=I(5.5))+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.text = element_text(size=I(13)),
        plot.title = element_text(hjust = 0.5,size = I(17)))+
  ggtitle("Job Involvement Percentage")+
  xlim(0.5,2.5)

ddsJobInvDonut   

# Attrition based on Job Involvement bar chart 
DDSAnalytics%>%ggplot(aes(x=JobInvolvement,fill=Attrition))+geom_bar()+
  ggtitle("Involvement Keeps Employees In The Company")+
  xlab("Job Involvement")+ylab("Employee Count")+
  theme(panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5,size=I(18)),
        axis.text = element_text(size = I(12)),
        axis.title = element_text(size=I(15)))

#panel.background = element_rect(fill = "lightblue",
                               # colour = "lightblue",

# Stock Option Level pie chart 
ddsStockOpt <- DDSAnalytics%>%group_by(StockOptionLevel)%>%
  dplyr::summarize(count=n(),
                   StockPercentage = n()/nrow(DDSAnalytics))
ddsStockPie <- ggplot(data=ddsStockOpt,aes(x=2,y=StockPercentage,fill=StockOptionLevel))+
  geom_col(color="white")+
  coord_polar("y",start = 1)+
  geom_text(aes(label=paste0(round(StockPercentage*100),"%")),
            position = position_stack(vjust = 0.5))+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        plot.title = element_text(hjust = 0.5,size = I(19)))+
  ggtitle("Percent Of Employees Owning Stock Option")
  
ddsStockPie

# Bar Chart Marital Status and Attrition Relationship
DDSAnalytics%>%ggplot(aes(MaritalStatus))+geom_bar(aes(fill=Attrition))+
  ggtitle("Higher Attrition Among Sinlge Employee")+xlab("Marital Status")+
  theme(plot.title = element_text(hjust = 0.5,size = I(17)))->AttvsMart
AttvsMart

figure <- ggarrange(Attdonut,ddsMaritalStatDonut,AttvsMart,
                    labels = c("A", "B","C"),
                    ncol = 3, nrow = 1)
figure

# Donut chart Relationship StockOptionLevel Attrition 'Yes'
DDSAnalytics%>%filter(Attrition=="Yes")->AttYes
AttYes%>%ggplot(aes(Attrition))+geom_bar(aes(fill=StockOptionLevel))+
  ggtitle("Employe Attrition Based On Stock Option ")+
  theme(plot.title = element_text(hjust = 0.5,size = I(20)),
        axis.text = element_text(size=I(17)),
        axis.title = element_text(size=I(18)),
        panel.background = element_blank(),
        axis.title.y = element_blank()
        )

 DDSAnalytics%>%ggplot(aes(StockOptionLevel))+geom_bar(aes(fill=MaritalStatus))+
    ggtitle("Not Enough Stock Option For Single Employee ")+
  theme(plot.title = element_text(hjust = 0.5,size = I(20)),
        axis.text = element_text(size=I(17)),
        axis.title = element_text(size=I(18)),
        panel.background = element_blank(),
        axis.title.y = element_blank()
        )

 library(dplyr)
 DDSAnalytics%>%ggplot(aes(JobInvolvement, fill= Attrition))+
  geom_bar() +
   ggtitle("Age Group Attrition ")+
  theme(plot.title = element_text(hjust = 0.5,size = I(20)),
        axis.text = element_text(size=I(17)),
        axis.title = element_text(size=I(18)),
        panel.background = element_blank()
       
        )

 DDSAnalytics%>%ggplot(aes(x=JobRole,fill=Attrition))+
   geom_bar()

DDSAnalytics%>%ggplot(aes(y=MonthlyIncome,x=Attrition,fill=Attrition))+
  geom_boxplot()+ggtitle("Lower Income Cause Of Attrition")+
  xlab("Attrition")+ylab("Monthly Income")+
  theme(panel.background =element_rect(fill = "white", colour = "grey50"),
        plot.title = element_text(hjust = 0.5,size=I(18)),
        axis.text = element_text(size=I(13)),
        axis.title = element_text(size=I(15)))

#Lower Monthlty Cause Of Attrition

 DDSAnalytics%>%ggplot(aes(y=MonthlyRate,x=Attrition,fill=Attrition))+
   geom_boxplot()+ggtitle("Lower Monthlty Rate Cause Of Attrition")+
  xlab("Attrition")+ylab("Monthly Rate")+
  theme(panel.background =element_rect(fill = "white", colour = "grey50"),
        plot.title = element_text(hjust = 0.5,size=I(18)),
        axis.text = element_text(size=I(13)),
        axis.title = element_text(size=I(15)),
        legend.position = "none")

# Monthly Income Attrition Jobrole boxplot  
DDSAnalytics%>%ggplot(aes(y=MonthlyIncome,x=Attrition,fill=JobRole))+geom_boxplot()+
  ggtitle("Job Role Income Analysis")+
  xlab("Attrition")+ylab("Monthly Income")+
  theme(panel.grid.major = element_line(color="grey70",size=0.2),
        panel.background = element_rect(fill = "white", colour = "grey50"),
        plot.title = element_text(hjust = 0.5,size=I(18)),
        axis.text = element_text(size=I(13)),
        axis.title = element_text(size=I(15))
        )

#MonthlyIncome,Attrition,JobRole ggpairs 
DDSAnalytics%>%dplyr::select(MonthlyIncome,Attrition,JobRole)%>%
  ggpairs(aes(color=JobRole))+
  ggtitle("Job Role Income Analysis Based On Attrition")+
  xlab("Attrition")+ylab("Monthly Income")+
  theme(panel.grid.major = element_line(color="grey70",size=0.2),
        panel.background = element_rect(fill = "white", colour = "grey50"),
        plot.title = element_text(hjust = 0.5,size=I(24)),
        axis.text = element_text(size=I(13)),
        axis.title = element_text(size=I(15))
        )
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Subseting data set based on Attrtion YES and NO
DDSAnalytics%>%ggplot(aes(OverTime))+geom_bar(aes(fill=Attrition))

DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=YearsSinceLastPromotion))+geom_point(aes(color=Attrition))

DDSAnalytics%>%ggplot(aes(Gender))+geom_bar(aes(fill=Attrition))

DDSAnalytics%>%filter(Attrition=="Yes")->AttYes
DDSAnalytics%>%filter(Attrition=="No")->AttNo

library(dplyr)

#Job Involvement Rating Percentage Attrition YES
AttYes$JobInvolvement = as.factor(AttYes$JobInvolvement)
AttYesJobInvCt <- AttYes%>%group_by(JobInvolvement)%>%
  dplyr::summarize(count=n(),
                   JobInvPercent = n()/nrow(AttYes))

jobInvYesAtt <- ggplot(data = AttYesJobInvCt,aes(x=2,y=JobInvPercent,fill=JobInvolvement))+
  geom_col(color="white")+
  coord_polar("y",start = 1)+
  geom_text(aes(label=paste0(round(JobInvPercent*100),"%")),
            position = position_stack(vjust = 0.5),size=I(5.5))+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.position = "none",
        legend.text = element_text(size=I(13)),
        plot.title = element_text(hjust = 0.5,size = I(17)))+
  ggtitle("Job Involvement Rating Percentage Attrition \nYes")+
  xlim(0.5,2.5)
jobInvYesAtt 

#Job Involvement Rating Percentage Attrition NO
AttNo$JobInvolvement = as.factor(AttNo$JobInvolvement)
AttNoJobInvCt <- AttNo%>%group_by(JobInvolvement)%>%
  dplyr::summarize(count=n(),
                   JobInvPercentAttNo = n()/nrow(AttNo))

jobInvNoAttDonut <- ggplot(data = AttNoJobInvCt,aes(x=2,y=JobInvPercentAttNo,fill=JobInvolvement))+
  geom_col(color="white")+
  coord_polar("y",start = 1)+
  geom_text(aes(label=paste0(round(JobInvPercentAttNo*100),"%")),
            position = position_stack(vjust = 0.5),size=I(4.5))+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.position = "none",
        legend.text = element_text(size=I(13)),
        plot.title = element_text(hjust = 0.5,size = I(17)))+
  ggtitle("Job Involvement Rating Percentage Attrition \nNo")+
  xlim(0.5,2.5)
jobInvNoAttDonut 

# Attrition Yes Over Time Analysis 
AttYesOvrtm <- AttYes%>%group_by(OverTime)%>%
  dplyr::summarize(count=n(),
                   OverTmPercent = n()/nrow(AttYes))
attOverTime <- ggplot(data = AttYesOvrtm,aes(x=3,y=OverTmPercent,fill=OverTime))+
  geom_col(color="white")+
  scale_fill_manual(values=c("#1E90FF", "#DC143C"))+
  coord_polar("y",start=1)+
  geom_text(aes(label=paste0(round(OverTmPercent*100),"%")),
            position = position_stack(vjust = 0.5),size=I(6),color="white")+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.text = element_text(size = I(13)),
        plot.title = element_text(hjust = 0.5,size=I(19))
        )+
  ggtitle("Over Time And Attrition \n YES")+
  xlim(0.1,3.5)
attOverTime 

# N0 Attrition vs Over Time
AttNoOvtm <- AttNo%>%group_by(OverTime)%>%
  dplyr::summarize(count=n(),
                   OverTmPercentNatt = n()/nrow(AttNo))
attOverTimeNatt <- ggplot(data = AttNoOvtm,aes(x=3,y=OverTmPercentNatt,fill=OverTime))+
  geom_col(color="white")+
  scale_fill_manual(values = c("#1E90FF", "#DC143C"))+
  coord_polar("y",start = 1)+
  geom_text(aes(label=paste0(round(OverTmPercentNatt*100),"%")),
            position = position_stack(vjust = 0.5),size=I(6),color="white")+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.text = element_text(size=I(13)),
        plot.title = element_text(hjust = 0.5,size = I(19)))+
  ggtitle("Over Time And Attrition \n NO")+
  xlim(0.1,3.5)
      
attOverTimeNatt

#Q. 4. The business is also interested in learning about any job role specific trends that may exist in the data set (e.g., “Data Scientists have the highest job satisfaction”). 
as.factor(DDSAnalytics$JobSatisfaction)->jobsatFac 
str(jobsatFac)
##  Factor w/ 4 levels "1","2","3","4": 4 3 4 4 4 1 3 4 3 3 ...
dfjobSat <- data.frame(jRol=DDSAnalytics$JobRole,Satisfaction=jobsatFac)
# ordering bars in ascending order
dfjobSat <- within(dfjobSat,
                   jRol <- factor(jRol,levels = names(sort(table(jRol),decreasing = FALSE))))
ggplot(dfjobSat,aes(x=jRol))+geom_bar(aes(fill=Satisfaction))+ggtitle("Job Role Vs Job Satisfaction")+
  xlab("Job Role")+ylab("Number  of Employee ")+coord_flip()+theme(plot.title = element_text(
    color="Black",
    hjust = 0.5,
    size = 18,
    
  ),axis.text.y = element_text(color="Black",
                               size = 15),
  axis.text.x = element_text(color="black",
                             size=15),
  axis.title.x = element_text(size = 17),
  axis.title.y = element_text(size = 17),
  panel.background = element_blank()
 )-> df2
df2

##Q.5. You can also provide any other interesting trends and observations from your analysis.
DDSAnalytics%>%ggplot(aes(x=YearsAtCompany,y=Age,color=JobRole))+
  geom_point(aes(size=I(2.3)))+ggtitle("Scatter Plot Of Job Role Based On Age And Years At The Company")+
  labs(x="Years at the company",y="Employee Age")+theme(
    plot.title = element_text(color = "#000000",
                              size = 12,
                              hjust = 0.5),
    axis.text = element_text(color = "#000000",
                             size = 14),
    axis.title = element_text(color = "#000000",
                              size=16),
    legend.text = element_text(color = "#000000",
                               size = 13),
    panel.background = element_blank()
  )

Attrition Classifcation Section

#DDSAnalyticsNoAttrition <-read.csv(file.choose())
DDSAnalyticsNoAttritionknn <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv",sep=",",header = TRUE)

DDSAnalyticsCfknn <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)


DDSAnalyticsCfknn%>%dplyr::select(MonthlyIncome, Age, NumCompaniesWorked,JobSatisfaction,JobLevel,YearsAtCompany,
                      TotalWorkingYears)->cl
clCor <- cor(cl)
corrplot(clCor,method = "number")

DDSAnalyticsCfknn$OverTime = as.numeric(DDSAnalyticsCfknn$OverTime)
DDSAnalyticsCfknn$Gender = as.numeric(DDSAnalyticsCfknn$Gender)
DDSAnalyticsCfknn$BusinessTravel= as.numeric(DDSAnalyticsCfknn$BusinessTravel)
DDSAnalyticsCfknn$MaritalStatus = as.numeric(DDSAnalyticsCfknn$MaritalStatus)
DDSAnalyticsCfknn$JobRole = as.numeric(DDSAnalyticsCfknn$JobRole)
DDSAnalyticsCfknn$MonthlyIncome_log = log(DDSAnalyticsCfknn$MonthlyIncome)
DDSAnalyticsCfknn$MonthlyRate_log = log(DDSAnalyticsCfknn$MonthlyRate)

kNN Classification

DDSAnalyticsCfknn$OverTime <- ifelse(DDSAnalyticsCfknn$OverTime=="yes",1,0)
mstatus <- as.data.frame(dummy.code(DDSAnalyticsCfknn$MaritalStatus))
jRol <- as.data.frame(dummy.code(DDSAnalyticsCfknn$JobRole))

knnclsDataframe <- data.frame(Attrition=DDSAnalyticsCfknn$Attrition,
                              JobInvolvement=DDSAnalyticsCfknn$JobInvolvement,
                              JobRole=jRol,
                              MaritalStatus = mstatus,
                              OverTime = DDSAnalyticsCfknn$OverTime,
                              StockOptionLevel= DDSAnalyticsCfknn$StockOptionLevel,
                              JobLevel=DDSAnalyticsCfknn$JobLevel,
                              JobSatisfaction=DDSAnalyticsCfknn$JobSatisfaction,
                              YearsAtCompany =DDSAnalyticsCfknn$YearsAtCompany,
                              YearsInCurrentRole=DDSAnalyticsCfknn$YearsInCurrentRole)


DDSAnalyticsNoAttritionknn$OverTime <- ifelse(DDSAnalyticsNoAttritionknn$OverTime=="yes",1,0)
mstatus <- as.data.frame(dummy.code(DDSAnalyticsNoAttritionknn$MaritalStatus))
jRol <- as.data.frame(dummy.code(DDSAnalyticsNoAttritionknn$JobRole))

knnPredictionDataframe <- data.frame(JobInvolvement = DDSAnalyticsNoAttritionknn$JobInvolvement,
                                     JobRole = jRol,
                                     MaritalStatus = mstatus,
                                     OverTime = DDSAnalyticsNoAttritionknn$OverTime,
                                     StockOptionLevel = DDSAnalyticsNoAttritionknn$StockOptionLevel,
                                     JobLevel = DDSAnalyticsNoAttritionknn$JobLevel,
                                     JobSatisfacton= DDSAnalyticsNoAttritionknn$JobSatisfaction,
                                     yearAtCompany = DDSAnalyticsNoAttritionknn$YearsAtCompany)

#kNN Classification cross validation

# spliting data set knnclsDataframe
set.seed(7)
ddsObsknn = sample(seq(1,dim(knnclsDataframe)[1]),round(.75*dim(knnclsDataframe)[1]),replace = FALSE)
ddsknnTrain = knnclsDataframe[ddsObsknn,]
ddsknnTest = knnclsDataframe[-ddsObsknn,]

# kNN crossvalidation 

AttClassification=knn(ddsknnTrain[,2:19],ddsknnTest[,2:19],ddsknnTrain$Attrition,prob = TRUE,k=17)
table(AttClassification,ddsknnTest$Attrition)
##                  
## AttClassification  No Yes
##               No  184  21
##               Yes   5   8
caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
## Confusion Matrix and Statistics
## 
##                  
## AttClassification  No Yes
##               No  184  21
##               Yes   5   8
##                                           
##                Accuracy : 0.8807          
##                  95% CI : (0.8301, 0.9206)
##     No Information Rate : 0.867           
##     P-Value [Acc > NIR] : 0.315662        
##                                           
##                   Kappa : 0.3254          
##                                           
##  Mcnemar's Test P-Value : 0.003264        
##                                           
##             Sensitivity : 0.9735          
##             Specificity : 0.2759          
##          Pos Pred Value : 0.8976          
##          Neg Pred Value : 0.6154          
##              Prevalence : 0.8670          
##          Detection Rate : 0.8440          
##    Detection Prevalence : 0.9404          
##       Balanced Accuracy : 0.6247          
##                                           
##        'Positive' Class : No              
## 
kCM = caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
kCM
## Confusion Matrix and Statistics
## 
##                  
## AttClassification  No Yes
##               No  184  21
##               Yes   5   8
##                                           
##                Accuracy : 0.8807          
##                  95% CI : (0.8301, 0.9206)
##     No Information Rate : 0.867           
##     P-Value [Acc > NIR] : 0.315662        
##                                           
##                   Kappa : 0.3254          
##                                           
##  Mcnemar's Test P-Value : 0.003264        
##                                           
##             Sensitivity : 0.9735          
##             Specificity : 0.2759          
##          Pos Pred Value : 0.8976          
##          Neg Pred Value : 0.6154          
##              Prevalence : 0.8670          
##          Detection Rate : 0.8440          
##    Detection Prevalence : 0.9404          
##       Balanced Accuracy : 0.6247          
##                                           
##        'Positive' Class : No              
## 

Finding Mean Accuracy knn

set.seed(1)
iterations = 100
numks = 60
splitPerc = .95

masterAcc = matrix(nrow = iterations, ncol = numks)

for(j in 1:iterations)
{
 ddsObsknn = sample(seq(1,dim(knnclsDataframe)[1]),round(.75*dim(knnclsDataframe)[1]),replace = FALSE)
ddsknnTrain = knnclsDataframe[ddsObsknn,]
ddsknnTest = knnclsDataframe[-ddsObsknn,]
  for(i in 1:numks)
  {
    AttClassification=knn(ddsknnTrain[,2:19],ddsknnTest[,2:19],ddsknnTrain$Attrition,prob = TRUE,k=i)
table(AttClassification,ddsknnTest$Attrition)

    kCM = caret::confusionMatrix(table(AttClassification,ddsknnTest$Attrition))
    masterAcc[j,i] = kCM$overall[1]
  }
  
}

MeanAcc = colMeans(masterAcc)

plot(seq(1,numks,1),MeanAcc, type = "l")

which.max(MeanAcc)
## [1] 18
max(MeanAcc)
## [1] 0.8431193
# # normalizing the values because Knn does the classification based on distances and it does better job when all the variables are normalized and are in same scalling. 
# normalize <-  function(x){
#   return((x-min(x))/(max(x)-min(x)))
# }
# #for example normalize(c(1,2,3,4,5))
# # for exampl normalize(c(20,30,40,50,100))
# # lets apply the function to the variables we want to normalize 
# # e.g:  iris_n <- as.data.frame (lapply(iris[,c(1,2,3,4)],normalize))
# 
# DDSAnalytics_normalized = as.data.frame(lapply(DDSAnalyticsCf[c(18,32,35,36,22,29,15,7)],normalize))
# summary(DDSAnalytics_normalized)
# dataFrame1 <- data.frame(
#                 YearsSinceLastPromotion = DDSAnalytics_normalized$YearsSinceLastPromotion,
#                 StockOptionLevel = DDSAnalytics_normalized$StockOptionLevel,
#                 NumCompaniesWorked = DDSAnalytics_normalized$NumCompaniesWorked,
#                 JobSatisfaction= DDSAnalytics_normalized$JobSatisfaction,
#                 JobInvolvement = DDSAnalytics_normalized$JobInvolvement,
#                 DistanceFromHome = DDSAnalytics_normalized$DistanceFromHome,
#                 Attrition = DDSAnalyticsCf$Attrition,
#                 Gender = DDSAnalyticsCf$Gender,
#                 MaritalStatus = DDSAnalyticsCf$MaritalStatus,
#                 BusinessTravelTravel= DDSAnalyticsCf$BusinessTravel)
# 
# library(class)
# library(caret)
# DDSAnalyticsCf$OverTime = as.numeric(DDSAnalyticsCf$OverTime)
# DDSAnalyticsCf$Gender = as.numeric(DDSAnalyticsCf$Gender)
# DDSAnalyticsCf$BusinessTravel= as.numeric(DDSAnalyticsCf$BusinessTravel)
# DDSAnalyticsCf$MaritalStatus = as.numeric(DDSAnalyticsCf$MaritalStatus)
# DDSAnalyticsCf$JobRole = as.numeric(DDSAnalyticsCf$JobRole)
# 
# lotistic <- glm(Attrition ~ YearsWithCurrManager + Age +YearsInCurrentRole+ YearsSinceLastPromotion + TotalWorkingYears + TrainingTimesLastYear+ WorkLifeBalance + YearsAtCompany + StockOptionLevel+PercentSalaryHike+ PerformanceRating+RelationshipSatisfaction+ NumCompaniesWorked+MonthlyIncome+ MonthlyRate+JobSatisfaction+JobInvolvement+HourlyRate+EnvironmentSatisfaction+DistanceFromHome+DailyRate+Gender+
#                   +JobLevel+ Education+MaritalStatus+EducationField+BusinessTravel+Department+JobRole, data = DDSAnalyticsCf, family = "binomial")
# summary(lotistic)

NaiveBayes Classification

DDSAnalyticsNoAttrition <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv", sep=",",header = TRUE)

DDSAnalyticsCf <- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)
# using naive Bayes on original data 

set.seed(7)
ddsObs = sample(seq(1,dim(DDSAnalyticsCf)[1]),round(.75*dim(DDSAnalyticsCf)[1]),replace = FALSE)
ddsTrain = DDSAnalyticsCf[ddsObs,]
dim(ddsTrain)
## [1] 652  36
ddsTest = DDSAnalyticsCf[-ddsObs,]
dim(ddsTest) 
## [1] 218  36
Navmodel1= naiveBayes(ddsTrain[,-c(1,3,23,10,11,17)],ddsTrain$Attrition)
#Navmodel1= naiveBayes(ddsTrain[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)],ddsTrain$Attrition)
table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition)
##      
##        No Yes
##   No  172   7
##   Yes  17  22
#table(predict(Navmodel1,ddsTest[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)]),ddsTest$Attrition)

CM1= caret::confusionMatrix(table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition))

#CM1= confusionMatrix(table(predict(Navmodel1,ddsTest[,c(13,15,16,17,18,19,20,21,24,29,33,35,36,34)]),ddsTest$Attrition))
CM1
## Confusion Matrix and Statistics
## 
##      
##        No Yes
##   No  172   7
##   Yes  17  22
##                                           
##                Accuracy : 0.8899          
##                  95% CI : (0.8406, 0.9282)
##     No Information Rate : 0.867           
##     P-Value [Acc > NIR] : 0.18590         
##                                           
##                   Kappa : 0.5835          
##                                           
##  Mcnemar's Test P-Value : 0.06619         
##                                           
##             Sensitivity : 0.9101          
##             Specificity : 0.7586          
##          Pos Pred Value : 0.9609          
##          Neg Pred Value : 0.5641          
##              Prevalence : 0.8670          
##          Detection Rate : 0.7890          
##    Detection Prevalence : 0.8211          
##       Balanced Accuracy : 0.8343          
##                                           
##        'Positive' Class : No              
## 
# Prediction 
navPred <- predict(Navmodel1,DDSAnalyticsNoAttrition)
library(modelr)
predictionDataFrame <- add_predictions(DDSAnalyticsNoAttrition,Navmodel1,var="navPred",type = NULL)


navClassification <- predictionDataFrame[,c(1,36)]
write.csv(navClassification,"NbAttritionClassification.csv")


# Donut chart Nab Classification on DDSAnalyticsNoAttrition data set 
nbClassification <- navClassification%>%group_by(navPred)%>%
  dplyr::summarize(count=n(),
                   NavPredPercent = n()/nrow(navClassification))

nbClassificationDonut <- ggplot(data = nbClassification,aes(x=3,y=NavPredPercent,fill=navPred))+
  geom_col(color="white")+
  scale_fill_manual(values = c("#1E90FF", "#DC143C"))+
  coord_polar("y",start = 1)+
  geom_text(aes(label=paste0(round(NavPredPercent*100),"%")),
            position = position_stack(vjust = 0.5),size=I(4.5),color="white")+
  theme(panel.background = element_blank(),
        axis.line = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.text = element_text(size=I(13)),
        plot.title = element_text(hjust = 0.5,size = I(17)))+
  ggtitle("naiveBayes Classification of Attrition")+
  xlim(0.1,3.5)
nbClassificationDonut + labs(fill="Attrition") # change the legend title to Attrition from default title

# 200 iteration on Training / Test split Percentage to calculate accuracy 
iterations = 200
masterAcc = matrix(nrow=iterations)
splitPerc = 0.75  #Training / Test split Percentage
for(j in 1:iterations){
  
ddsObs = sample(seq(1,dim(DDSAnalyticsCf)[1]),round(splitPerc*dim(DDSAnalyticsCf)[1]))
ddsTrain = DDSAnalyticsCf[ddsObs,]
ddsTest = DDSAnalyticsCf[-ddsObs,]

Navmodel1= naiveBayes(ddsTrain[,-c(1,3,23,10,11,17)],ddsTrain$Attrition,laplace = 1)
table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition)
CM1= caret::confusionMatrix(table(predict(Navmodel1,ddsTest[,-c(1,3,23,10,11,17)]),ddsTest$Attrition))
masterAcc[j] = CM1$overall[1] 
}
MeanAcc = colMeans(masterAcc)
MeanAcc
## [1] 0.832844

#logistic regression

#ddsPredictionData <- read.csv(file.choose())
ddsPredictionData<- read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2CompSet%20No%20Attrition.csv",sep = ",",header = TRUE)


DDSAnalyticsLogistic <-read.csv("https://raw.githubusercontent.com/krisGTech/DDS_FINAL_PROJECT/master/Data/CaseStudy2-data.csv",sep = ",",header = TRUE)


DDSAnalyticsLogistic$OverTime = as.factor(DDSAnalyticsLogistic$OverTime)
set.seed(7)
ddsObslogis = sample(seq(1,dim(DDSAnalyticsLogistic)[1]),round(.75*dim(DDSAnalyticsLogistic)[1]),replace = FALSE)
ddsLogisTrain = DDSAnalyticsLogistic[ddsObslogis,]
dim(ddsLogisTrain)
## [1] 652  36
ddsLogisTest = DDSAnalyticsLogistic[-ddsObslogis,]
dim(ddsLogisTest) 
## [1] 218  36
logisModClf <- glm(Attrition ~ JobInvolvement+JobLevel+JobRole+JobSatisfaction+MaritalStatus+MonthlyIncome+MonthlyRate+
                     OverTime+StockOptionLevel+YearsAtCompany+YearsSinceLastPromotion+
                     YearsWithCurrManager+YearsInCurrentRole,
                    data = ddsLogisTrain, family=binomial(link="logit"))
summary(logisModClf)
## 
## Call:
## glm(formula = Attrition ~ JobInvolvement + JobLevel + JobRole + 
##     JobSatisfaction + MaritalStatus + MonthlyIncome + MonthlyRate + 
##     OverTime + StockOptionLevel + YearsAtCompany + YearsSinceLastPromotion + 
##     YearsWithCurrManager + YearsInCurrentRole, family = binomial(link = "logit"), 
##     data = ddsLogisTrain)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.92289  -0.55313  -0.27780  -0.09874   2.97668  
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    3.441e-01  1.109e+00   0.310 0.756246    
## JobInvolvement                -7.887e-01  1.789e-01  -4.409 1.04e-05 ***
## JobLevel                      -3.760e-01  4.482e-01  -0.839 0.401495    
## JobRoleHuman Resources         1.101e+00  8.326e-01   1.323 0.185906    
## JobRoleLaboratory Technician   1.010e+00  6.758e-01   1.494 0.135213    
## JobRoleManager                 1.932e-01  1.062e+00   0.182 0.855658    
## JobRoleManufacturing Director -1.236e+00  9.205e-01  -1.343 0.179254    
## JobRoleResearch Director      -1.360e+00  1.402e+00  -0.970 0.332129    
## JobRoleResearch Scientist      3.898e-01  6.750e-01   0.577 0.563617    
## JobRoleSales Executive         8.252e-01  5.868e-01   1.406 0.159653    
## JobRoleSales Representative    1.499e+00  7.438e-01   2.016 0.043838 *  
## JobSatisfaction               -5.026e-01  1.185e-01  -4.242 2.22e-05 ***
## MaritalStatusMarried           1.379e+00  4.423e-01   3.118 0.001822 ** 
## MaritalStatusSingle            1.876e+00  5.375e-01   3.490 0.000483 ***
## MonthlyIncome                  4.877e-05  1.187e-04   0.411 0.681311    
## MonthlyRate                   -1.439e-05  1.810e-05  -0.795 0.426539    
## OverTimeYes                    1.797e+00  2.677e-01   6.713 1.91e-11 ***
## StockOptionLevel              -1.044e-01  2.012e-01  -0.519 0.603812    
## YearsAtCompany                 1.131e-02  4.633e-02   0.244 0.807129    
## YearsSinceLastPromotion        2.335e-01  5.887e-02   3.965 7.33e-05 ***
## YearsWithCurrManager          -1.428e-01  6.184e-02  -2.309 0.020924 *  
## YearsInCurrentRole            -1.570e-01  6.332e-02  -2.479 0.013176 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 594.98  on 651  degrees of freedom
## Residual deviance: 416.65  on 630  degrees of freedom
## AIC: 460.65
## 
## Number of Fisher Scoring iterations: 6
varImp(logisModClf) # displays all the important variables 
##                                 Overall
## JobInvolvement                4.4092415
## JobLevel                      0.8389543
## JobRoleHuman Resources        1.3227870
## JobRoleLaboratory Technician  1.4938581
## JobRoleManager                0.1819037
## JobRoleManufacturing Director 1.3430570
## JobRoleResearch Director      0.9698350
## JobRoleResearch Scientist     0.5774769
## JobRoleSales Executive        1.4062395
## JobRoleSales Representative   2.0156343
## JobSatisfaction               4.2418899
## MaritalStatusMarried          3.1178152
## MaritalStatusSingle           3.4900623
## MonthlyIncome                 0.4106743
## MonthlyRate                   0.7951278
## OverTimeYes                   6.7125276
## StockOptionLevel              0.5189261
## YearsAtCompany                0.2441314
## YearsSinceLastPromotion       3.9653132
## YearsWithCurrManager          2.3093618
## YearsInCurrentRole            2.4789736
pred = predict(logisModClf, newdata=ddsLogisTest,type = "response")

table(ddsLogisTest$Attrition,pred > 0.5)
##      
##       FALSE TRUE
##   No    181    8
##   Yes    13   16
ddsPredictionData$OverTime=as.factor(ddsPredictionData$OverTime)
predLogistic <- predict(logisModClf,newdata = ddsPredictionData,type="response")

# Accuracy = 90.3%
# Sensitivity = 93.3%
# Specificity = 66.666%
#https://www.shrm.org/hr-today/trends-and-forecasting/special-reports-and-expert-views/Documents/Retaining-Talent.pdf

# Turnover Analysis
# How Many Are Leaving?
# Who Is Leaving?
# Costs and Benefits?
#What causes turnover?
# satisfactry pay, good working condition and development opportunites.
# low job satisfaction 
# poor performance 
# more attractive than his or her current role
# work environment
# what make employee stay?
# positive work environment,
# promotional opportunities, status
# in the community, 
lotisticCF <- glm(Attrition ~ Age+EnvironmentSatisfaction+JobInvolvement+JobSatisfaction+MaritalStatus+
            RelationshipSatisfaction+TrainingTimesLastYear+WorkLifeBalance+YearsSinceLastPromotion+
              JobRole+ MonthlyIncome+MonthlyRate+PerformanceRating+StandardHours+StockOptionLevel,
              data = DDSAnalyticsCf, family = "binomial")
summary(lotisticCF)
## 
## Call:
## glm(formula = Attrition ~ Age + EnvironmentSatisfaction + JobInvolvement + 
##     JobSatisfaction + MaritalStatus + RelationshipSatisfaction + 
##     TrainingTimesLastYear + WorkLifeBalance + YearsSinceLastPromotion + 
##     JobRole + MonthlyIncome + MonthlyRate + PerformanceRating + 
##     StandardHours + StockOptionLevel, family = "binomial", data = DDSAnalyticsCf)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9929  -0.5836  -0.3547  -0.1711   2.8553  
## 
## Coefficients: (1 not defined because of singularities)
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    2.839e+00  1.440e+00   1.971 0.048678 *  
## Age                           -2.821e-02  1.375e-02  -2.051 0.040288 *  
## EnvironmentSatisfaction       -1.808e-01  9.462e-02  -1.911 0.056022 .  
## JobInvolvement                -7.043e-01  1.423e-01  -4.949 7.47e-07 ***
## JobSatisfaction               -3.869e-01  9.386e-02  -4.122 3.76e-05 ***
## MaritalStatusMarried           9.284e-01  3.698e-01   2.510 0.012060 *  
## MaritalStatusSingle            1.570e+00  4.555e-01   3.447 0.000568 ***
## RelationshipSatisfaction      -1.107e-01  9.248e-02  -1.197 0.231314    
## TrainingTimesLastYear         -2.597e-01  8.883e-02  -2.924 0.003457 ** 
## WorkLifeBalance               -4.047e-01  1.434e-01  -2.823 0.004754 ** 
## YearsSinceLastPromotion        7.122e-02  3.624e-02   1.965 0.049378 *  
## JobRoleHuman Resources         9.603e-01  6.963e-01   1.379 0.167854    
## JobRoleLaboratory Technician   6.490e-01  5.167e-01   1.256 0.209127    
## JobRoleManager                -1.399e-01  9.133e-01  -0.153 0.878246    
## JobRoleManufacturing Director -1.590e+00  8.296e-01  -1.916 0.055324 .  
## JobRoleResearch Director      -1.298e+00  1.204e+00  -1.078 0.281082    
## JobRoleResearch Scientist      5.475e-01  5.146e-01   1.064 0.287317    
## JobRoleSales Executive         4.580e-01  4.532e-01   1.011 0.312170    
## JobRoleSales Representative    1.798e+00  5.747e-01   3.129 0.001755 ** 
## MonthlyIncome                 -2.395e-05  6.408e-05  -0.374 0.708549    
## MonthlyRate                   -9.877e-06  1.488e-05  -0.664 0.506750    
## PerformanceRating              1.868e-01  2.857e-01   0.654 0.513240    
## StandardHours                         NA         NA      NA       NA    
## StockOptionLevel              -9.890e-03  1.823e-01  -0.054 0.956730    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 767.67  on 869  degrees of freedom
## Residual deviance: 608.81  on 847  degrees of freedom
## AIC: 654.81
## 
## Number of Fisher Scoring iterations: 6

Conclusion:

In nutshell, company is always going to have attrition, some are voluntary and some involuntary. The most important thing we are focus on this project is to find out a way to retain employee as much as possible. Voluntary attrition such as retirement, personal health, relocation etc. can be very costly to the company. On the other hand, involuntary attrition such as layoffs or reductions in force and terminating poorly performing employees can sometime be beneficial as it could create an opportunity to hire high performance and motivated employee that can better much a job role. While some of the factors that is analyzed in this project are not so easy to control attrition, there are some factors such as over time, stock option and job involvement that I believe can be controlled to retain the talented employees. Reducing overtime whenever its possible or spreading hours equally among several employee could be a solution to elevate the motivation and the performance of employee. As we all like to get appreciated and get involve in company and get noticed often, I am certain that focusing on improving the job involvement could be an excellent step to take to keep attrition in balance.